Bacterial Foraging Optimization Algorithm

In the grand theatre of human civilisation, every era is blessed with a rare few who do not merely walk the path already lit-but become the light itself. There are scientists who study the universe-and then there are visionaries who attempt to re-imagine it entirely. Satish Gajawada, IIT Roorkee alumnus, independent researcher, and self-styled Architect of Infinite AI Horizons, belongs resolutely to the second tribe. His life's work is not a quiet contribution to an existing body of knowledge; it is a thunderous declaration that the boundaries of Artificial Intelligence have barely been drawn, and that there exist entire galaxies of thought still waiting to be named. To encounter Satish's story is to walk through a corridor lined with the lanterns he himself ignited-new fields of AI coined by his mind, new branches of knowledge sprouted from his pen, and a philosophy of service rooted in the oldest and most sacred of human duties: the love of a child for his parents.

The accurate parameter identification of three-phase induction motors (IM) certainly becomes fundamental to the design of high-performance motor drive systems, the real-time digital simulation models and predictive maintenance platforms. This paper presents a systematic comparative study between two methodologically distinct approaches in other to identify the per-phase equivalent circuit parameters of a 5 HP (3.73 kW), 415 V, 50 Hz, four-pole, three-phase squirrel-cage IM: the classical experimental test method (covering: DC resistance test, no-load test and blocked-rotor test in accordance with the IEEE 112) and then a bio-inspired Bacterial Foraging Optimization Algorithm (BFOA). The experimental method yields five key parameters — stator resistance (R1 = 1.143 Ω), rotor resistance referred to stator (Rr = 0.729 Ω), stator and rotor leakage reactances (X1 = X2 = 1.534 Ω), magnetizing reactance (Xm = 11.81 Ω) and the core-loss resistance (Rc = 107.8 Ω) — under idealized test conditions that necessitate several simplifying assumptions. Clearly, the BFOA treats all the five parameters simultaneously as decision variables within a bounded search space. This is seeded by experimental values, minimizing a multi-attributes objective function which penalizes prediction errors in the stator input current, electromagnetic torque and input power across multiple load points. The results obtained demonstrate that the BFOA-refined parameter set reduces the overall model prediction error by almost 96.3%, from J(φ) = 0.0842 based on the experimental parameters to J(φ*) = 0.0031 after the optimization. Also, there is a huge improvement in the accuracy of the torque-speed characteristic, power factor prediction and efficiency characteristics. Ten characteristic plots are presented and analyzed in detail. The plots illustrate both the motor’s fundamental performance behavior and the superiority of the BFOA-identified parameters over the experimentally derived baseline.

In the era of data-driven decision-making, data warehouses provide the foundational infrastructure for intelligent systems by enabling the execution of complex analytical queries over massive datasets. However, optimizing query performance remains a persistent challenge, particularly in selecting efficient indexes under strict storage constraints. A central bottleneck in this context is the Bitmap Join Index Selection Problem (BJI-SP), an NP-hard combinatorial problem involving the identification of an optimal subset of bitmap join indexes (BJIs). This study proposes the Improved Binary Artificial Fish Swarm Algorithm (IBAFSA), a novel metaheuristic specifically tailored to address the binary nature of the BJI-SP. IBAFSA extends the classical Artificial Fish Swarm Algorithm (AFSA) through three key innovations: (1) a hybrid initialization strategy combining greedy heuristics with chaotic transformations to generate a diverse and high-quality initial population; (2) adaptive parameter control, incorporating Lévy flights for global exploration, dynamic step-length adjustment for local refinement, and variable visual distance for a balanced exploration-exploitation trade-off; and (3) binary-specific behavioral operators redefined using Hamming distance and probabilistic bit-flipping. Experimental results on benchmark OLAP workloads show that IBAFSA consistently outperforms GA, BPSO, and DM-based methods in terms of gain rate, convergence speed, and execution time. These findings confirm the scalability, robustness, and effectiveness of IBAFSA for index selection in large-scale data warehouse environments.

This paper presents a Swarm based Cooperation Mechanism for scheduling optimization. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to support decision making in agile manufacturing environments. Agents coordinate their actions automatically without human supervision considering a common objective – global scheduling solution taking advantages from collective behavior of species through implicit and explicit cooperation. The performance of the cooperation mechanism will be evaluated consider implicit cooperation at first stage through ACS, PSO and ABC algorithms and explicit through cooperation mechanism application.

Medical imaging mainly treats and processes missing, ambiguous, complementary, redundant and distorted data. Biomedical image registration is the process of geometric overlaying or alignment of two or more 2D/3D images of the same scene, taken at different time slots, from different angles, and/or by different acquisition systems. In medical practice, it is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Technically, image registration implies a complex optimization of different parameters, performed at local or/and global levels. Local optimization methods frequently fail because functions of the involved metrics with respect to transformation parameters are generally nonconvex and irregular. Therefore, global methods are often required, at least at the beginning of the procedure. In this paper, a new evolutionary and bio-inspired approach -bacterial foraging optimization -is adapted for single-slice to 3-D PET and CT multimodal image registration. Preliminary results of optimizing the normalized mutual information similarity metric validated the efficacy of the proposed method by using a freely available medical image database.

Se presenta una metodología para cuantificar el efecto de la distorsión armónica de tensión sobre la eficiencia, el factor de potencia y la potencia del motor trifásico de inducción. La metodología usa un circuito equivalente modificado del motor de inducción que permite calcular su operación ante frecuencias armónicas. Las ecuaciones del modelo se resuelven usando el principio de superposición y son simuladas mediante el software MATLAB. La metodología fue aplicada a un motor de jaula de ardilla de 3 HP. Se observa que el factor de potencia y la eficiencia del motor disminuyen respecto a la magnitud y el orden del contenido armónico del sistema de alimentación, por lo tanto la metodología permite determinar la desclasificación de la potencia del motor.

The world has become more and more dependent upon oil based products, derived from petroleum. Large volume of oil is stored and transported through water transport. Sometimes in transport, oils are spilled onto level or in water. Spillage of oil can seriously affect the marine environment as a result of physical challenges and toxic effects. When an oil spill occurs the damage may be limited and extend over a small area or be extensive and encompass a large area. In all of these situations, it is important for scientists, citizens and government officials to know what to expect and what action need to be taken when spills occur. Bacterial Foraging Optimization Algorithm is considered for solving the problem of oil spillage in marine. The proposed work focuses on remotely locating the spill areas in marine with the use of automatic robotic swarms termed as Aquaboats. These aquaboats are supposed to enter the sea or ocean and tend to move randomly over the area to locate the spill. And once the spill got in contact with the boundary of aquaboats, it stops and corresponding results are tabulated

Currently, due to the industrial progress in the world and the increase in population numbers led to the occurrence of an economic crisis as well as environmental pollution; for which there are microgrid advances capabilities and the use of renewable sources, for production the energy at high quality and lowest cost possible. So, there are many effective algorithms can be utilized for a purpose schedule energy production among available sources of energy in the microgrid system. A microgrid with two diesel generators, two wind turbines and three fuel-cell plants has been used as a case study. Firefly algorithm (FA) nature-inspired algorithms are among the most efficient algorithm for optimization, the proposed method used to optimize schedule generation in a microgrid for 24 hours at minimum cost. Moreover, the results are validated by performing various tests of the algorithm parameters and have proven that the method is efficiently done for scheduling optimum power generation in the microgrid.

Se presenta una metodología para cuantificar el efecto de la distorsión armónica de tensión sobre la eficiencia, el factor de potencia y la potencia del motor trifásico de inducción. La metodología usa un circuito equivalente modificado del motor de inducción que permite calcular su operación ante frecuencias armónicas. Las ecuaciones del modelo se resuelven usando el principio de superposición y son simuladas mediante el software MATLAB. La metodología fue aplicada a un motor de jaula de ardilla de 3 HP. Se observa que el factor de potencia y la eficiencia del motor disminuyen respecto a la magnitud y el orden del contenido armónico del sistema de alimentación, por lo tanto la metodología permite determinar la desclasificación de la potencia del motor.

In this work the application of a method to estimate the shaft power of induction motors operating in the presences of unbalanced and harmonics voltages is presented. The method uses the steady state equivalent circuits, with some considerations for the analysis of motor performance, fed with unbalanced and harmonic voltages. The parameters of circuits are determined with low invasiveness, by applying a Bacterial Foraging Algorithm as technique of evolutionary search. With this, the shaft power can be estimated at any operating point. The method was tested in a 12.6 kW motor working in an industrial power supply grid, with harmonics and unbalanced voltage.

In this work the application of a method to estimate the shaft power of induction motors operating in the presences of unbalanced and harmonics voltages is presented. The method uses the steady state equivalent circuits, with some considerations for the analysis of motor performance, fed with unbalanced and harmonic voltages. The parameters of circuits are determined with low invasiveness, by applying a Bacterial Foraging Algorithm as technique of evolutionary search. With this, the shaft power can be estimated at any operating point. The method was tested in a 12.6 kW motor working in an industrial power supply grid, with harmonics and unbalanced voltage.

VHAAI and ISVHAAI stands for Very Highly Advanced Artificial Intelligence and International Society for Very Highly Advanced Artificial Intelligence respectively. This is the ISVHAAI Artificial Intelligence Society Letter No. 7 in which a novel algorithm titled Family Human Swarm Optimization (FHSO) is designed.

The Study focuses on supply chain management and performance: An experience from university of Nigeria teaching hospital Enugu State, Nigeria. Specifically the study aimed to pursue the following objectives: To determine the effect of strategic supplier partnership on goal attainment of University of Nigeria teaching hospital Enugu State, Nigeria , establish the nature of the relationship between customer relationship and customer satisfaction of University of Nigeria teaching hospitrds al Enugu State, Nigeria and ascertain the effect of quality of information sharing on employee performance of University of Nigeria teaching hospital Enugu State, Nigeria. The study had a population size of (1168) employees of University Nigeria teaching hospital Enugu State, Nigeria. Instruments used for data collection were primary questionnaires and interview. The total number of 396 copies of the questionnaire were distributed, 354 copies were returned & used while 42 copies were not properly filled. Survey research design was adopted for the study. Three hypotheses were tested using Pearson product moment correlation coefficient and simple linear regression tool. The findings indicated that A strategic supplier partnership positively and significantly affect goal attainment of University of Nigeria teaching hospital Enugu State (r = 0.894; F= 1408.540; t = 37.531; P< 0.05). There was a significant positive relationship between customer relationship and customer satisfaction of University of Nigeria teaching hospital Enugu State (r =.851, P<.05). Quality of information sharing positively and significantly affect employee performance of University of Nigeria teaching hospital Enugu State (r = 0.874; F = 171.811; t = 33.721; P< 0.05). The study concluded that healthcare supply chain management involves obtaining resources, managing supplies, and delivering goods and services to providers and patients. The study recommends that Health institutions should always establish a long-term relationship between supplier and patients allowing for the free flow of feedback and ideas. Over time, this will create a more streamlined, effective supply chain that could have a positive impact on both costs, customer service, and goal attainment.

The countenance recognition presents a challenging problem in the field of image analysis and computer vision. Also, it received a good attention over the last few years. The applications of countenance recognition in the areas such as person tracking, surveillance, protection, entertainment, theft prevention, as easily as in the growth of human machine interfaces. It has been shown that precision in countenance is crucial to countenance recognition algorithms. An error of a few pixels in countenance will produce an important reduction in countenance recognition rates. In this paper, two approaches are proposed for use in countenance recognition of human. Firstly, Bacterial Foraging Optimization (BFO) and secondly, Particle Swarm Optimization (PSO). The Bacterial Foraging Optimization in which the features extracted from Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) are optimized. The Particle Swarm Optimization (PSO), which optimizes the transform coefficients acquired from the Discrete Wavelet Transform (DWT) of the images. LDA and ICA are based on global impendence whereas DWT is performed on a block by block basis exploring the local impendence based features. The classifier performance and the length of the selected feature vector are pondering for performance evaluation using the Indian face database . In this paper experimental results indicate that using Particle Swarm Optimization with DWT feature selection algorithm to proliferate transcendent countenance recognition outcome.

Mobile Ad-hoc Network (MANET) is an accumulation of movable nodes organizing a irregular topology without centralized administration. In a MANET, multicasting is a significant technique for utilizing data communication system. Multicasting based enhanced proactive source routing is proposed in this paper for Mobile Ad hoc Networks. It explains an innovative multicasting algorithm that considers the transmission energy and residual energy while forwarding the data packets. It improves the network throughput and raises the network lifetimes. Simulation analysis is carried in this proposed system and this method shows improved performance over the existing system.

In this paper, we present swarm intelligence algorithms for pedestrian tracking. In particular, we present a modified Bacterial Foraging Optimization (BFO) algorithm and show that it outperforms PSO in a number of important metrics for pedestrian tracking. In our experiments, we show that BFO's search strategy is inherently more efficient than PSO under a range of variables with regard to the number of fitness evaluations which need to be performed when tracking. We also compare the proposed BFO approach with other commonly-used trackers and present experimental results on the CAVIAR dataset as well as on the difficult PETS2010 S2.L3 crowd video.

Particle Swarm Optimization (PSO) is a popular and widely used optimization algorithm for solving complex problems. It is known for its simplicity and ease of implementation. Artificial Birds move in search space to find optimal solution. Although many PSO algorithms were proposed in literature the concepts like happiness and health are not yet explored in PSO algorithms. This article is based on this research gap. Happiness and Health Particle Swarm Optimization (HaHePSO) algorithm is created by incorporating the Happiness and Health concepts into Particle Swarm Optimization algorithm. Each particle in HaHePSO algorithm is associated with happiness and health variables. The movement of Artificial Birds in PSO algorithm is based on fitness values. In HaHePSO algorithm the movement of Artificial Birds is dependent on happiness, health and fitness values. In PSO algorithm Artificial Birds move in the direction of local best and global best of fitness values. This idea is extended in HaHePSO algorithm where Artificial Birds move in the direction of local best and global best of happiness, health and fitness values. The HaHePSO algorithm proposed in this article takes more space and requires extra computation compared to PSO algorithm. This is due to the fact that each particle now has happiness and health variables associated with it and movement in search space is guided by the fitness, happiness and health values.

Varias razones justifican el esfuerzo en determinar los parámetros de un motor asincrónico. Una de ellas puede ser la evaluación de la eficiencia operacional mediante el método del circuito equivalente. La eficiencia del motor se deteriora cuando trabaja en redes con tensiones desbalanceadas y, por otra parte, en estas condiciones la determinación de los parámetros del motor se dificulta ya que es necesario tener en cuenta la variación que sufren las impedancias del rotor con la ferecuencia del campo de secuencia negativa. En este trabajo se propone la utilización de algoritmos genéticos para identificar estos parámetros sin necesidad de pruebas especiales convirtiéndose en una poderosa herramienta a utilizar en condiciones de campo. Los resultados de la simulación se validan comparándolos con la práctica en un motor empleado como prototipo. Palabras clave: Algoritmos genéticos, motor asincrónico, parámetros, desbalance de tensión One of the goals of parameter identification in asynchronous motors is efficiency evaluation in field conditions. According to the equivalent circuit method, knowing the parameters of the park circuit in the operating range of the machine, adapted to the power quality of field conditions, corresponding calculation are made. In unbalanced voltage condition, there are some complexities because there are two sets of parameters belonging to the positive and negative sequence networks differing fundamentally in the rotor circuit. This paper, with the application of genetic algorithm (GA), a new approach is proposed to identified the parameters of both sequences combining the powerful proprieties of GA with one initial vector of parameters that allows reduce the search time without needs special proofs of some complexity that in field conditions are very expensive or impossible to do.

The Graph Coloring Using Bacterial Foraging Optimization (BFO) algorithm efficiently addresses the graph coloring problem by ensuring adjacent nodes are assigned distinct colors. Utilizing BFO, we conducted experiments on graphs ranging from 10 to 100 nodes, demonstrating its effectiveness and scalability. Results show that the algorithm consistently achieved valid colorings with minimal chromatic numbers: 3 colors for 10 and 25 nodes, 6 colors for 50 and 75 nodes, and 7 colors for 100 nodes. Execution times were notably short, with the fastest at 0.004464 seconds for 10 nodes and 0.007576 seconds for 100 nodes. These findings underscore BFO's computational efficiency and suitability for graph coloring tasks across various graph sizes, suggesting its potential for broader applications in optimization and graph theory.

هلاقم تاعلاطا هدیکچ لماک یشهوژپ هلاقم :تفایرد 17 یدرورف ن 1396 :شریذپ 05 ادرخ د 1396 :تیاس رد هئارا 13 دادرم 1396 هنیهب بقع هب ماگ رلرتنک ود درکلمع هسیاقم هب هلاقم نیا رلرتنک و تارذ ماحدزا متیروگلا اب هدش LQR رواه تلاح رد روتورداوک کی یور رب یم لدم ادتبا رد .تسا تیمها زئاح نآ یارب لااب ییاراک اب هنیهب یاهرلرتنک هعسوت و تسین رادیاپ یکیمانید متسیس کی روتورداوک .دزادرپ هیبش روظنم هب تلاح یاضف تلاداعم و هدش یفرعم روتروداوک یکیمانید یم هئارا یکیمانید لدم یزاس .دنوش و بقع هب ماگ رلرتنک ود سپس

Generally, Job Shop Scheduling Problem (JSSP) is an operational sequencing problem to process n jobs on m machines in a given sequence so as to optimally utilize the resources by complete processing of all jobs in a minimum possible time. JSSP has belongs to the category of NP hard problems where the search space of the problem is (n!) m. Several naturally inspired evolutionary techniques / methods have recently been developed to address these problems to get near optimal solutions in a reasonable time period thus several unsolved / difficulty to solve JSSPs became target for many researchers. Some Traditional Optimization Techniques includes Priority Dispatch Rules, Efficient Methods solvable in polynomial times. Enumerative methods and Mathematical formulation like Linear Programming, Mixed Linear Programming, Lagrangian Relaxation, Branch and Bound and Disjunctive Graph techniques etc. the research has been accelerated with applications of Nontraditional Techniques. Application of Artificial Intelligence, insertion Algorithms, Bottleneck Heuristics, Neural Networks, Expert Systems and Local Search algorithms. Application of Evolutionary Algorithms for scheduling found from mid 80s to till date. Genetic Algorithm, Variation in GAs, PSO, ACO, BCO, Memetic Algorithm, Immune Algorithm and several Hybrid Algorithms. The BFO algorithms, HS algorithms, IWO have been found applied in JSSP in the recent years. More focus on the application of Non Traditional methods for JSSP is increasing compared to traditional techniques. For the same cost and time non traditional methods yield better solutions compared to traditional methods. Hence, in this paper classical evolutionary algorithms namely Invasive weed optimization (IWO), Bacterial Foraging Optimization (BFO) and Music Based Harmony Search principles and fine-tuned the mechanisms to model and solve JSSP. Several Bench Mark instances available in OR library were thoroughly tested to prove the efficiency of the proposed methods by selective and random generation of populations. Two different types of population generations were considered i) Random Population (RP): The initial population is randomly generated and applied to the algorithm procedures and let us call such methods as PSO with RP, HSPO with RP, AIA with RP, HAIA with RP, MBHS with RP, IMBHS with RP, BFO and IWO with RP. ii) Selective Population: The initial Populations are generated using priority dispatching rules. A priority dispatching rule is a simple mathematical formula that, based on some processing parameters, specifies the priority of operations to be executed. 10 initial schedules i.e., populations are generated using 10 commonly used priority dispatching rules given in the Table 1 let us call these methods as

This paper presents an educational software tool for aid the teaching of Particle Swarm Optimization (PSO) fundamentals with friendly design interface. This software were developed in the platform of LabVIEW (Laboratory Virtual Intrumentation Engineering Workbench). The software‟s best qualities are users can select many different version of the PSO algorithm, a lot of the benchmarks test functions for optimization and set the parameters that have an influence on the PSO performance. Through visualization of particle distribution in the searching, the simulator is particularly effective in providing users with an intuitive feel for the PSO algorithm. Keyword : Benchmark Function, LabVIEW, Particle Swarm Optimization, PSO Algorithm

El proyecto de investigación que se propone tiene como propósito evaluar los métodos empleados en el análisis de factibilidad de propuestas, para el mejoramiento de la eficiencia energética en motores trifásicos (MTs). El proyecto se fundamenta en la necesidad de mejorar la eficiencia operacional de los MTs, pues estos representan casi el 50% del consumo de energía eléctrica mundial y son los mayores consumidores de energía eléctrica en el sector industrial. En la literatura científica se reportan varios estudios de factibilidad de propuestas para el mejoramiento de la eficiencia en estos equipos. Estos análisis, sin embargo, se basan en métodos de estimación que no consideran aspectos relevantes de la operación de los MTs, como las condiciones reales de carga y de suministro eléctrico, que pueden implicar errores significativos en la evaluación del impacto de las medidas propuestas. En el presente proyecto estos métodos se evaluarán experimentalmente en las cuatro etapas siguientes...

Research on metahurestic for solving optimization problems has been appeared as a great subject of interest during last few years. It involves modeling the natural phenomena of various species foraging for the food and as well as theory of natural evolution of species. In this paper we discuss three major metahurestic approaches for optimization problems appeared in software testing such as path prioritization, automatic test case generation, test case selection etc. First we discuss Ant colony optimization as suggested by Grasse in 1959 and later modeled by Dorigo, Maniezzo, and Colorni in 1996 as one of the optimization algorithm for solving optimization problems. Second focus is on natural inspired phenomena of Honey bee colony suggested by V. Tereshko, based on Reaction-diffusion diffusion model of a honeybee colony's foraging behavior. Finally we end up with genetic algorithm inspired by theory of evolution for solving optimization problems. The survey results the potential use of mentioned metahurestic approaches in software testing.

We are developing an idea through this paper which would give any question the perfect and best answer. Existing system is not capable of classifying according to different patterns. This compromises with efficiency of the system and quality of final result. In this project Parallel Clustering optimization method is formed by amalgamation of Map-Reduce with Ant Bee Colony Optimization Technique for improving the efficiency and success of the data science method. In addition, related running services on a Hadoop network are predicted with the help of Map Reduce algorithm.

Due to interference phenomena among unnatural dimensions of the motion robots' operations space, optimal path planning of them has to satisfy not just one criterion, but rather multi-objects. In this paper, we propose a novel multi-object approach for optimal mobile robot path planning, based on bees pollen optimizer (BPO). We consider two objects of distance and smooth path of the special plan for motion robots for constructing a minimization one. In operation environment for action robots, the location of the target and the obstacles are set up for the solution of BPO. The selected sequence of the mobile robot is a set of the chosen global best settlement in each iteration, which updates its archived data throughout the movement for motion robots in order. A series of simulations are executed in some environments for the best pathway once the robot reaches its goal. The results indicate that the proposed approach offered the robot path to its target without touching the obstacles, and the proposed method may be an alternative approach to optimize the motion robot path planning.

In this paper an Adaptive Bacterial Foraging is proposed for fuzzy entropy optimization when it is applied to the segmentation of gray images. The proposed algorithm represents the improved version of classical bacterial foraging algorithm which is a newly developed stochastic optimization tool. This optimization technique is applied for optimization of the fitness function which is fuzzy entropy. Classical bacterial foraging algorithm is improved by adaptively selecting the exploitation and exploration state in chemotaxis of E.coli. bacteria. The newly developed algorithm is applied on benchmark gray images and proved to be suitable for thresholding based image segmentation.

The organic Rankine cycle solar–thermal power system is a promising concept for the distributed energy market. The present work investigates the automatic generation control of an autonomous hybrid energy system based on organic Rankine cycle solar–thermal technology. Any mismatch between the active power generation and consumption in the hybrid energy system manifests itself as a frequency deviation from the nominal system frequency. To confront the frequency excursion problem, the present work considers automatic generation control, in which the controllers’ parameters are optimized by using particle swarm optimization (PSO). The performance of each controller is compared with its genetic algorithm (GA)‐optimized counterpart with respect to its ability to capture the frequency variation in the proposed hybrid energy system. The simulation results reveal that the performance of the PSO‐optimized controller is better than its GA‐optimized counterpart in terms of settling time and ov...

Present work presents small signal stability analysis of an autonomous hybrid energy system with dish-Stirling solar thermal systems (DSTS) in integration with diesel engine generators (DEG), fuel cells (FC), battery energy storage system (BESS), and aqua electrolyzer (AE). The performance of Genetic algorithm (GA) optimized integral (I), proportional plus integral (PI), and proportional-integral-derivative (PID) controllers in containing the frequency deviation in the proposed system has been investigated. The dynamic performance of all three controllers, so optimized, is compared with manually tuned I controller. Simulation results revealed that the performance of the GA optimized PID controller is found to be the best amongst all three controllers. Further, sensitivity analysis is carried out to access the robustness of the controllers.

Cervical cancer is a common cancer that affects women all over the world. This is the fourth leading cause of death among women and has no symptoms in its early stages. At the cervix, cervical cancer cells develop slowly. If it can be detected early, this cancer can be successfully treated. Health professionals are now facing a major challenge in detecting such cancer until it spreads rapidly. This study applied various machine learning classification methods to predict cervical cancer using risk factors. The main aim of this research work is to be described of the performance variation of eight most classifications algorithm to detect cervical cancer disease based on the selection of various top features sets from the dataset. Multilayer Perceptron (MLP), Random Forest and k-Nearest Neighbor, Decision Tree, Logistic Regression, SVC, Gradient Boosting, AdaBoost are examples of machine learning classification algorithms that have been used to predict cervical cancer and help in early...

Today"s world is very much concerned to reduce green house gas emission from the conventional thermal power plants as cutting down emissions from transport and heating sector may not be realistic in the near future. To reduce pollution from electrical power sources, the world is now marching towards usage of renewable energy sources (RESs). These sources being small in capacity are mostly connected at the distribution voltage level. This indirectly reduces transmission and distribution losses as the sources are around the load. This distribution system having small scale energy sources is called as a microgrid or active distribution network. Microgrid operates generally in a grid connected mode. However, circumstances such as fault, voltage sag and large frequency oscillations in the main grid may force the active distribution network to be disconnected from the main grid and operate as an isolated microgrid. During this isolation there will be change in power output from the controllable microsources which are to be regulated properly to have a stable operation in regard to power balance and frequency of operation within the isolated microgrid. An autonomous isolated microgrid comprising both controllable & uncontrollable sources. Like solar, wind, diesel generator (DG), aqua electrolyzer (AE), fuel cell (FC), a battery energy storage system (BESS), and fly wheel (FW) are considered. Solar, wind, DG and FC are power generating source & BESS, FW, AE as energy storage element. The generated hydrogen by an AE is used as fuel for a FC. The power system frequency deviates for the sudden change in load demand and the real power generation. The output power of DG, FC, BESS, FW and power absorbed by AE is regulated by using controller such that frequency of the system is controlled. Controller used is proportional plus integral plus derivative (PID). Load Disturbance Rejection (LDR) is used for tuning of controller gains of the proposed hybrid system. This uses the chien-hornes-resnick (CHR) setting with 20% overshoot. Design of p-f droop (frequency regulation parameter) for different controllable source in microgrid using bode plot stability criterion. The system response with Modified LDR based controller, LDR based controller and classical controller are compared. Investigation shows that Modified LDR based controller gives best response amongst these three methods.

Collective intelligence (CI) means that a group of people or animals can solve problems efficiently and offer greater insight and a better answer than any individual could provide. Fuzzy sets have been integrated with collective intelligence techniques in order to allow uncertain, vague imprecise and incomplete information to be incorporated to the CI models. The fuzzy CI techniques have been rarely used in the solution of energy problems even they still present new research opportunities to researchers. This chapter gives the results of the literature review on fuzzy CI research for energy systems. Keywords Collective intelligence Á Fuzzy sets Á Performance Á Energy systems Particle swarm optimization 22.1 Introduction Collective Intelligence is the wisdom of crowds introduced by Francis Galton. Collective intelligence is based on the fact that groups of people can be more intelligent than an intelligent individual and that groups do not always require intelligent people to reach a smart decision or outcome. Several collective intelligence techniques have been proposed in the literature.

Fuel is the most cost component of thermal power generation. The output power of each plant should not exceed the needs of the consumer's load. Unoptimal generation between generating units can cause power losses along the transmission line so that the fuel drain is large enough. The purpose of this study was to determine the optimal power output combination of each generating unit and determine the minimum total fuel cost of the combined power output of the plant by taking into account the valve-point effect and the operational constraint (economic load dispatch). The Newton-Raphson method is used to calculate the transmission line power losses. In this research, Bacterial Foraging Algorithm (BFA) optimization method is proposed to solve the problem of economic load dispatch. This method uses natural selection of optimum global bacterium which has a good food search strategy in the fitness function. The effectiveness of BFAs is tested on IEEE 5 bus 3 generatorsystem and IEEE 30 bus 6 generator system. The results of this proposed method show the optimal value of both generating systems.

The development of hybrid algorithms is becoming an important topic in the global optimization research area. This article proposes a new technique in hybridizing the particle swarm optimization (PSO) algorithm and the Nelder-Mead (NM) simplex search algorithm to solve general nonlinear unconstrained optimization problems. Unlike traditional hybrid methods, the proposed method hybridizes the NM algorithm inside the PSO to improve the velocities and positions of the particles iteratively. The new hybridization considers the PSO algorithm and NM algorithm as one heuristic, not in a sequential or hierarchical manner. The NM algorithm is applied to improve the initial random solution of the PSO algorithm and iteratively in every step to improve the overall performance of the method. The performance of the proposed method was tested over 20 optimization test functions with varying dimensions. Comprehensive comparisons with other methods in the literature indicate that the proposed solution method is promising and competitive.

The paper presents a real time modelling of self-reliant isolated grid comprising both governable and ungovernable sources. The frequency of this system must be maintained to its desired value by reducing its deviation occurred when there is a mismatch between the load demand and the actual power generation. In order to achieve it, the actual power output of the micro-grid is required to regulate. Therefore, an attempt is made in this paper to design a modified bias (MB) with LDR (load disturbance rejection) based PID (proportional integral derivative) Controller and it can also be called droop based controller (DBC). Simulation of the micro-grid system with proposed MB-LDR based control scheme is successfully done in Real Time Simulation platform using the digital simulator of OPAL-RT OP4510. Simulation results of the system for different conditions are presented and analysis is given in details. Thereafter, comparative performance of the results obtained using proposed MB-LDR base...

Today"s world is very much concerned to reduce green house gas emission from the conventional thermal power plants as cutting down emissions from transport and heating sector may not be realistic in the near future. To reduce pollution from electrical power sources, the world is now marching towards usage of renewable energy sources (RESs). These sources being small in capacity are mostly connected at the distribution voltage level. This indirectly reduces transmission and distribution losses as the sources are around the load. This distribution system having small scale energy sources is called as a microgrid or active distribution network. Microgrid operates generally in a grid connected mode. However, circumstances such as fault, voltage sag and large frequency oscillations in the main grid may force the active distribution network to be disconnected from the main grid and operate as an isolated microgrid. During this isolation there will be change in power output from the controllable microsources which are to be regulated properly to have a stable operation in regard to power balance and frequency of operation within the isolated microgrid. An autonomous isolated microgrid comprising both controllable & uncontrollable sources. Like solar, wind, diesel generator (DG), aqua electrolyzer (AE), fuel cell (FC), a battery energy storage system (BESS), and fly wheel (FW) are considered. Solar, wind, DG and FC are power generating source & BESS, FW, AE as energy storage element. The generated hydrogen by an AE is used as fuel for a FC. The power system frequency deviates for the sudden change in load demand and the real power generation. The output power of DG, FC, BESS, FW and power absorbed by AE is regulated by using controller such that frequency of the system is controlled. Controller used is proportional plus integral plus derivative (PID). Load Disturbance Rejection (LDR) is used for tuning of controller gains of the proposed hybrid system. This uses the chien-hornes-resnick (CHR) setting with 20% overshoot. Design of p-f droop (frequency regulation parameter) for different controllable source in microgrid using bode plot stability criterion. The system response with Modified LDR based controller, LDR based controller and classical controller are compared. Investigation shows that Modified LDR based controller gives best response amongst these three methods.

Backpropagation algorithm is used to solve many real world problems using the concept of Multilayer Perceptron. However, the main disadvantages of Backpropagation are its convergence rate is relatively slow, and it is often trapped at the local minima. To solve this problem, in literatures, evolutionary algorithms such as the Particle Swarm Optimization algorithm has been applied in feedforward neural network to optimize the learning process in terms of convergence rate and classification accuracy but this process needs longer training time. To provide alternative solutions, in this study, Bacterial Foraging Optimization Algorithm has been selected and applied in feedforward neural network to enhance the learning process in terms of convergence rate and classification accuracy. One of the main processes in Bacterial Foraging Optimization algorithm is the chemotactic movement of a virtual bacterium that makes a trial solution of the optimization problem. This process of chemotactic movement is guided to make the learning process of Artificial Neural Network faster. The developed Bacterial Foraging Optimization Algorithm Feedforward Neural Network is compared against Particle Swarm Optimization Feedforward Neural Network. The results show that Bacterial Foraging Optimization Algorithm gave a better performance in terms of convergence rate and classification accuracy compared to Particle Swarm Optimization Feedforward Neural Network.

In this work the application of a method to estimate the shaft power of induction motors operating in the presences of unbalanced and harmonics voltages is presented. The method uses the steady state equivalent circuits, with some considerations for the analysis of motor performance, fed with unbalanced and harmonic voltages. The parameters of circuits are determined with low invasiveness, by applying a Bacterial Foraging Algorithm as technique of evolutionary search. With this, the shaft power can be estimated at any operating point. The method was tested in a 12.6 kW motor working in an industrial power supply grid, with harmonics and unbalanced voltage.

Varias razones justifican el esfuerzo en determinar los parámetros de un motor asincrónico. Una de ellas puede ser la evaluación de la eficiencia operacional mediante el método del circuito equivalente. La eficiencia del motor se deteriora cuando trabaja en redes con tensiones desbalanceadas y, por otra parte, en estas condiciones la determinación de los parámetros del motor se dificulta ya que es necesario tener en cuenta la variación que sufren las impedancias del rotor con la ferecuencia del campo de secuencia negativa. En este trabajo se propone la utilización de algoritmos genéticos para identificar estos parámetros sin necesidad de pruebas especiales convirtiéndose en una poderosa herramienta a utilizar en condiciones de campo. Los resultados de la simulación se validan comparándolos con la práctica en un motor empleado como prototipo. Palabras clave: Algoritmos genéticos, motor asincrónico, parámetros, desbalance de tensión One of the goals of parameter identification in asynchronous motors is efficiency evaluation in field conditions. According to the equivalent circuit method, knowing the parameters of the park circuit in the operating range of the machine, adapted to the power quality of field conditions, corresponding calculation are made. In unbalanced voltage condition, there are some complexities because there are two sets of parameters belonging to the positive and negative sequence networks differing fundamentally in the rotor circuit. This paper, with the application of genetic algorithm (GA), a new approach is proposed to identified the parameters of both sequences combining the powerful proprieties of GA with one initial vector of parameters that allows reduce the search time without needs special proofs of some complexity that in field conditions are very expensive or impossible to do.

Data analysis harmonic measurement variable in time RESUMEN Este artículo tiene como objetivo revisar los problemas asociados a la medición de los niveles de armónicos presentes, en una onda distorsionada de tensión y corriente no estacionaria, y efectuar la caracterización de los datos registrados. Para ello, se utiliza como ejemplo un grupo de mediciones recientes, realizadas en condiciones de campo a un motor asincrónico trifásico de 320 kW, 440 V, 820 A, 750 rpm, cuya velocidad se regula con un convertidor de frecuencia, y se determina la distorsión de tensión y corriente. Estos datos son descritos en diferentes formas estadísticas. Las ventajas y desventajas de cada una de ellas son discutidas, así como la interpretación de los resultados. Se analiza, además, los factores que influyen en el factor de distorsión de tensión (FDT). Las observaciones realizadas en el trabajo son aplicables a cualquier armónico individual o al THD. Palabras clave Análisis estadísticos, distribución normal, medición de armónicos, señal variable en el tiempo.

Se analiza el origen de los armónicos de tiempo en los sistemas eléctricos, tanto de los múltiplos integrales como de los no integrales de la fundamental (interarmónicos y subarmónicos). Se consideran las causas y los efectos fundamentales de los mismos sobre las pérdidas en los motores asincrónicos. A partir de un estudio de caso, se evalúa el comportamiento de un motor en presencia de los principales armónicos y a partir del espectro armónico de un circuito del CAI Ciudad Caracas, se ponen en evidencia los efectos negativos que sobre un motor conectado en el mismo tendría esa distorsión armónica. Se establece, que para evaluar estos efectos, resulta conveniente emplear métodos de inteligencia artificial. Palabras clave: Calidad de la energía, eficiencia energética, motores asincrónicos The source of time harmonics in electric systems, no only integral multiples, but also non multiples of the fundamental (inter and subharmonics), are analyzed. The main causes and effects on induction motor losses are considered. On the base of a case study, the operation of an induction motor considering the most important harmonics is evaluated. From a harmonic spectrum, the negative effects that would have a motor connected to a circuit of the sugar factory "Ciudad Caracas" are shown. The convenience of using tolls based on artificial intelligence for evaluating these effects is remarked

Nature has always inspired human race in solving various problems. This chapter, in particular, shows a detailed overview of various biologically and nature-inspired multi-agent systems as proposed in the existing relevant literature. A detailed description and analysis of the proposed multi-agent strategies are provided. Multi-agent systems have been in various spheres of management. The applications in terms of cases and situations where these multi-agent systems are applicable are also mentioned along with the introduction of the various agent-based systems. The purpose of this chapter is to assist the readers to think about simulating the existing bio-inspired phenomena with the help of agent-based systems.

The Photo Voltaic Systems (PVS) has become a very important source of renewable energy sources, as a result of the global energy crisis as well as environmental pollution resulting from conventional energy sou rces. One of the most important applications of (PVS) are those connected to the electrical grid, despite th e instability problems facing these systems. This paper studies the stability analysis of PVS connected to g rid by using two modern techniques of artificial intelligence based Proportional-Integral (PI) controller. The se two modern techniques used in this paper are a Firefly Algorithm (FA), and Adaptive Acceleration Parti cle Swarm Optimization (AAPSO) technique in order to voltage stability of PVS connected to three phase electrical distribution network for any rang of solar radiation. This paper uses the MATLAB / SIMULINK software, it has been implemented for the comparison of the conventional PI controllers, FA, and AAPSO used for photovoltaic DC voltage control. This comparison has been carried out to compare the settling ti mes and overshoots of each technique, to find out whichever is most efficient for this purpose.

The Photo Voltaic Systems (PVS) has become a very important source of renewable energy sources, as a result of the global energy crisis as well as environmental pollution resulting from conventional energy sources. One of the most important applications of (PVS) are those connected to the electrical grid, despite the instability problems facing these systems. This paper studies the stability analysis of PVS connected to grid by using two modern techniques of artificial intelligence based Proportional-Integral (PI) controller. These two modern techniques used in this paper are a Firefly Algorithm (FA), and Adaptive Acceleration Particle Swarm Optimization (AAPSO) technique in order to voltage stability of PVS connected to three phase electrical distribution network for any rang of solar radiation. This paper uses the MATLAB / SIMULINK software, it has been implemented for the comparison of the conventional PI controllers, FA, and AAPSO used for photovoltaic DC voltage control. This c...

Co-Authorship I hereby declare that this thesis incorporates material that is result of joint research of the author and his supervisors Prof. Ahmed Azab and Prof. Fazle Baki. Chapters 3, 4, 5, 6, 7 of the thesis was co-authored with Prof. Ahmed Azab and Prof. Fazle Baki. In all the cases, the key ideas, primary contributions, experimental design, data analysis, interpretation and writing were performed by the author; Prof. Ahmed Azab and Prof. Fazle Baki provided feedback on refinement of ideas and editing of the manuscript. This joint research has been submitted to Journals and Conferences that are listed below. I am aware of the University of Windsor Senate Policy on Authorship, and I certify that I have properly acknowledged the contribution of other researchers to my thesis, and have obtained written permission from Prof. Ahmed Azab and Prof. Fazle Baki to include the above material(s) in my thesis. I certify that, with the above qualification, this thesis, and the research to which it refers, is the product of my own work. II.

Adopting Nature inspired optimization algorithms for image processing is on the double growth in the last decade. Artificial Bee Colony (ABC) approach is highly potential nature inspired optimization method mimics the bee's foraging behaviour. Moreover, the popularity of classification and artificial intelligence in different fields leads to the employment of ABC algorithm in upsurge. Notably, Early detection of breast cancer through digital mammogram images is essential as it is the one the most common cause of humankind cancer deaths. the aim of this comprehensive survey was to methodically analyse the effectiveness of using ABC algorithm in medical image enhancement, segmentation, and classification. This study firstly gives introduction of ABC algorithm and its basic mathematical and biological principles and operations respectively. Furthermore, this academic study summarizes the ABC applications on image segmentation techniques like Otsu and image classification approaches like Artificial Neural Networks (ANN). Finally, this far-reaching study come up with the challenges while exercising ABC algorithm in medical image processing, especially in mammogram images.

This paper proposes a simple and efficient method for designing a class of circularly-symmetric spatial linear filters implemented on cellular neural networks. The design method relies on a so-called 1-D prototype filter, with desired characteristics and on a 1-D to 2-D spatial frequency transformation. Several design examples are given, for 2-D low-pass and band-pass filters (both of FIR and IIR type) with imposed cut-off or peak frequency and a specified selectivity. Finally, simulation results are provided, on a real grayscale biomedical image.