Bat Algorithm

The proliferation of metaheuristic algorithms for solving complex
optimization problems has necessitated robust and standardized
benchmarking practices. A fair and comprehensive evaluation is
crucial for validating an algorithm's performance, understanding
its strengths and weaknesses, and guiding future algorithmic
development. This paper provides a comprehensive review of the
landscape of benchmarking in metaheuristic optimization. We
systematically categorize and detail the primary types of
benchmark problems, including the classic set of 23 mathematical
functions, real-world engineering problems, specialized CEC
benchmark suites, combinatorial optimization benchmarks, and
multi-objective problems. For each category, we present the
fundamental mathematical formulations and discuss their specific
characteristics and challenges. Furthermore, we outline the
standard evaluation metrics used to quantify algorithmic
performance. Finally, we discuss current challenges in
benchmarking, such as the no free lunch theorem, the issue of
overfitting to test suites, and the need for more diverse and realworld
benchmarks. This review serves as a foundational guide for
researchers and practitioners in selecting appropriate
benchmarks and conducting rigorous, reproducible evaluations of
metaheuristic algorithms.

The Cuckoo Search Algorithm (CSA), introduced by Xin-She Yang and Suash Deb in 2009, is a nature-inspired metaheuristic optimization
technique modeled on the brood parasitism behavior of certain cuckoo bird species. Utilizing a Levy flight mechanism, CSA effectively
balances global exploration and local exploitation, making it a versatile tool for addressing non-linear, multi-modal, and high-dimensional
optimization problems.
This paper presents a comprehensive exploration of CSA, detailing its biological foundation, mathematical framework, and algorithmic
processes. Key modifications, including hybrid approaches, adaptive mechanisms, and domain-specific enhancements, are reviewed to
illustrate how CSA has been refined to tackle increasingly complex optimization challenges. Applications spanning engineering, machine
learning, energy systems, robotics, and telecommunications highlight CSA’s versatility and efficiency in solving real-world problems.
Despite its strengths, challenges such as parameter sensitivity and computational demands in large-scale scenarios persist. To address these,
avenues for future research are proposed, including the integration of CSA with emerging technologies like quantum computing and advanced
machine learning techniques. This study underscores CSA’s role as a cornerstone of modern metaheuristic optimization, offering
a robust framework for solving diverse and challenging problems.

Automatic Generation Control (AGC) is critically important for the frequency control and tie-line power flow regulation of modern energetic and interlinked electricity systems. AGC is of fundamental importance because of the added sophistication of power networks arising from renewable energy sources integration and multiple control area interconnections. This paper presents a selected survey of standalone and hybrid control techniques executed between 2001 to 2025, all aimed at achieving AGC especially in multi-region interlinked systems. The selected control methods comprise of the combination of classical control methods: like Proportional-Integral (PI) and PI-Derivative (PID) controllers, soft computing or intelligent methods as well as those that are metaheuristic based: like Firefly Algorithm (FA), Artificial Neural Network (ANN), Fuzzy Logic (FL), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and other associated approaches. Furthermore, this paper provides up to date information on the primary issues and advancements that require attention for reliable and intelligent AGC implementation, and will be relevant to enthusiasts and stakeholders in this area.

Image enhancement is one of the most critical subjects in computer vision and image processing fields. It can be considered as means to enrich the perception of images for human viewers. All kinds of images typically suffer from different problems such as weak contrast and noise. The primary purpose of image enhancement is to change an image's visual appearance. Many algorithms have recently been proposed for enhancing medical images. Image enhancement is still deemed a challenging task. In this paper, the fuzzy c means clustering (FCM) technique is utilized to enhance the medical images. The method of enhancement consists of two stages. The proposed algorithm conducts a cluster test on the image pixels. It then increases the difference of gray level between the diverse objects to accomplish the enhancement purpose of the medical images. The experimental results have been tested using various images. The algorithm enhanced the small target of the image to a reasonable limit and revealed favorable performance. The results of image enhancement techniques were evaluated by using terms of different criteria such as peak signal to noise ratio (PSNR), mean square error (MSE) and average information contents (AIC), showing promising performance.

In this work, it was proposed to compress the color image after de-noise by proposing a coding for the discrete transport of new wavelets called discrete chebysheve wavelet transduction (DCHWT) and linking it to a neural network that relies on the convolutional neural network to compress the color image. The aim of this work is to find an effective method for face recognition, which is to raise the noise and compress the image in convolutional neural networks to remove the noise that caused the image while it was being transmitted in the communication network. The work results of the algorithm were calculated by calculating the peak signal to noise ratio (PSNR), mean square error (MSE), compression ratio (CR) and bit-per-pixel (BPP) of the compressed image after a color image (256×256) was entered to demonstrate the quality and efficiency of the proposed algorithm in this work. The result obtained by using a convolutional neural network with new wavelets is to provide a better CR with the ratio of PSNR to be a high value that increases the high-quality ratio of the compressed image to be ready for face recognition.

Recommender systems are new types of internet-based software tools, designed to help users find their way through today's complex on-line shops and entertainment websites. Here we provide an overview of current recommender systems, and then outline a new Lifestyle Recommender System, which employs techniques such as evolutionary search and a 3D avatar to provide tailored and friendly suggestions for users.

The object of research are decision support systems. The subject of research is the decision-making process in management problems using bio-inspired algorithms. A method for the search of solutions in the field of national security using bio-inspired algorithms is proposed. The proposed method is based on a combination of an artificial bat algorithm and evolving artificial neural networks. The method has the following sequence of actions: ‒ input of initial data; ‒ processing of initial data taking into account the degree of uncertainty; ‒ numbering of bat agents (BA); ‒ placement of bat agents taking into account the degree of uncertainty about the state of the analysis object in the search space; ‒ setting the initial BA speed and the echolocation frequency of each BA; ‒ starting a local search; ‒ launching a global search; ‒ training knowledge bases of bat agents. The originality of the proposed method consists in the arrangement of bat agents taking into account the uncertainty...

This article presents load frequency control for a nonlinear multi-source power system divided into three areas, consisting of thermal reheat power plants, hydropower, and wind generation, while considering generation rate constraints (GRC). A proportional-integral-derivative (PID) plus secondorder derivative (PID+DD) controller optimized using the chess algorithm (CA) is proposed. The effectiveness of CA is validated against hippopotamus optimization (HO), grey wolf optimizer (GWO), and ant lion optimizer (ALO) under two scenarios: a 10% step load perturbation (SLP) and a random load pattern (RLP). Simulation results indicate that the proposed CA significantly improves dynamic performance. In scenario 1 (10% SLP), CA achieves a reduction of approximately 30.5% in integral weight time absolute error (ITSE) compared to GWO and 43.7% compared to HO, while also reducing frequency undershoot in Area 2 by 15.2% compared to HO. In scenario 2 RLP, CA maintains robustness, limiting tieline power deviations to ±8 MW, whereas HO exhibits deviations exceeding ±12 MW. Overall, the CA-tuned PID+DD controller demonstrates superior damping, reduced overshoot and undershoot, and enhanced stability across multi-area interconnected renewable systems, making it a promising approach for future real-time load frequency control (LFC) applications with higher renewable penetration.

This article presents load frequency control for a nonlinear multi-source power system divided into three areas, consisting of thermal reheat power plants, hydropower, and wind generation, while considering generation rate constraints (GRC). A proportional-integral-derivative (PID) plus secondorder derivative (PID+DD) controller optimized using the chess algorithm (CA) is proposed. The effectiveness of CA is validated against hippopotamus optimization (HO), grey wolf optimizer (GWO), and ant lion optimizer (ALO) under two scenarios: a 10% step load perturbation (SLP) and a random load pattern (RLP). Simulation results indicate that the proposed CA significantly improves dynamic performance. In scenario 1 (10% SLP), CA achieves a reduction of approximately 30.5% in integral weight time absolute error (ITSE) compared to GWO and 43.7% compared to HO, while also reducing frequency undershoot in Area 2 by 15.2% compared to HO. In scenario 2 RLP, CA maintains robustness, limiting tieline power deviations to ±8 MW, whereas HO exhibits deviations exceeding ±12 MW. Overall, the CA-tuned PID+DD controller demonstrates superior damping, reduced overshoot and undershoot, and enhanced stability across multi-area interconnected renewable systems, making it a promising approach for future real-time load frequency control (LFC) applications with higher renewable penetration.

In rural areas, grid expansions and diesel generators are commonly used to provide electricity, but their high maintenance costs and CO2 emissions make renewable energy sources (RES) a more practical alternative. Traditional methods such as analytical, statistical, and numerical-based techniques are inadequate for designing an energy-efficient RES. Therefore, this study utilized the bat algorithm (BA) to optimize the use of hybrid RES for rural electrification. A feasibility study was conducted in the village of Kalema to assess energy consumption, and a diesel-only system was modeled to serve the entire community. The BA was used to determine the optimal size and cost-effectiveness of the hybrid RES, with MATLAB R (2021a) utilized for simulation. The BA's performance was compared with diesel only and GA using cost of energy (COE) and CO2 emissions as metrics. Diesel generators only produced a COE of $6,562,000 and 1679.6 lb/hr of CO2 emissions. COE with BA was $356,9781.37 (a 45.6% reduction) and CO2 emissions were 635.29 lb/hr (a 62.2% drop). Genetic algorithm (GA) resulted in $364,3122.46 COE and 652.69 lb/hr CO2 emissions, indicating 61.1% and 44.5% decreases, respectively. BA significantly reduced COE and CO2 emissions over GA, according to the analysis.

High technical losses, poor voltage regulation, and overloaded transformers remain major challenges in many Nigerian distribution networks, including the Sapele 11 kV feeder. This study evaluated the integration of solar photovoltaic (PV)-based distributed generation (DG) as a technical solution for improving network performance. A simulation-based approach was adopted using ETAP for load-flow analysis and the Newton–Raphson method for steady-state evaluation, while the Bat Algorithm was applied to determine the optimal location and size of DG units. The assessment focused on voltage profile, active power loss, reactive power loss, and transformer loading before and after DG integration. The results showed that DG integration significantly improved network operation. The minimum bus voltage increased from 87.82% to above 97%, total active power loss dropped from 1013.84 kW to 93.11 kW, and reactive power loss was also markedly reduced. In addition, heavily overloaded transformers were relieved, with critical units reduced to acceptable loading levels. These findings indicate that optimally allocated solar PV-based DG can strengthen distribution network performance, improve service reliability, and provide a practical support option for weak radial feeders in Nigeria.

This study is focused on as Cuckoo Search (CS), one of the current meta-heuristic optimization algorithm. The CS algorithm is useful in generating and searching for the most optimum particles of important meta-heuristic optimization algorithm, known as the Particle Swarm Optimization (PSO), to enhance its performance. This optimization is confirmed through a benchmark online optimization and actual problems. The PSO algorithm performance is also compared with differing algorithms representative of the area. The CS optimal solutions outperform alternative current solutions as CS has distinct search features. The study findings have implications for future studies and practice.

Efficient repression of noise in a medical image is a very significant issue. This paper proposed a method to denoise medical images by the use of a hybrid adaptive algorithm based on the bat algorithm (BA) and genetic algorithm (GA). Medical images can be often affected by different kinds of noise that decrease the precision of any automatic system for analysis. Therefore, the noise reduction methods are always utilized for increasing the Peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) of images to optimize the originality. Gaussian noise and salt and pepper noise corrupted the used medical data, separately. The noise level to medical images was added noise variance from 0.1 to 0.5 to compare the performance of the de-noising techniques. In the analytical study, we apply different kinds of noise like Gaussian noise and salt-and-pepper noise to medical images for making these images noisy. The hybrid BA-GA model was applied on medical noisy image...

A design of an optimal backstepping fractional order proportional integral derivative (FOPID) controller for handling the trajectory tracking problem of wheeled mobile robots (WMR) is examined in this study. Tuning parameters is a challenging task, to overcome this issue a hybrid meta-heuristic optimization algorithm has been utilized. This evolutionary technique is known as the hybrid whale grey wolf optimizer (HWGO), which benefits from the performances of the two traditional algorithms, the whale optimizer algorithm (WOA) and the grey wolf optimizer (GWO), to obtain the most suitable solution. The efficiency of the HWGO algorithm is compared against those of the original algorithms WOA, GWO, the particle swarm optimizer (PSO), and the hybrid particle swarm grey wolf optimizer (HPSOGWO). The simulation results in MATLAB–Simulink environment revealed the highest efficiency of the suggested HWGO technique compared to the other methods in terms of settling and rise time, overshoot, a...

Economic dispatch (ED) is a critical optimization problem in power systems, aiming to schedule generator outputs to meet load demand at minimal cost. Traditional formulations often model ED as a quadratic equation; however, the problem is inherently nonconvex due to factors such as ramp-rate limits, valve-point loading, and prohibited operating zones. These complexities, coupled with the increasing scale of power grids, pose significant challenges for traditional optimization techniques. This paper introduces a modified bat algorithm (MBA) designed to enhance both exploration and exploitation capabilities for solving the ED problem. The proposed MBA incorporates adaptive parameter control and an elitist learning strategy inspired by Adaptive Particle Swarm Optimization (APSO) to improve robustness and convergence. The performance of the MBA is evaluated on benchmark test systems, and the results are compared against those obtained using the original BA, genetic algorithm (GA), and particle swarm optimization (PSO). The results demonstrate improvements in convergence speed, solution quality, and robustness, making it a promising candidate for advanced economic dispatch optimization.

This paper proposes a novel smart virtual rotor controller (VRC) that combines the Bat Algorithm (BA) with extreme learning machine (ELM) to enhance frequency stability in power systems. To reflect the impact of renewable integration, inverter-based power plants are incorporated to simulate high levels of penetration from power-electronics-based generation. The proposed method first tunes the virtual rotor parameters (virtual inertia and damping control) using BA under varying operating conditions. These parameters are then trained with ELM to enable adaptive control across different scenarios. Time-domain simulations demonstrate that the proposed approach outperforms existing methods in terms of frequency nadir and settling time, while also achieving a significant reduction in execution time, requiring only 0.0033 seconds.

This paper gives you the overview of the subfield of artificial intelligence which is called as "SWARM INTELLIGENCE". This is so called because it is named after the inspiration taken from the working behavior of the swarms. The term "swarm" denotes the group or aggregation of insects or animals or birds which works together so as to complete the difficult tasks in an efficient manner which are not possible to be completed as an individual entity. This working behavior of swarms has influenced the researchers to solve their problems in robotics, telecommunications, computer science, networking and various other technical fields. Apart from it, algorithms have been proposed to solve various complex problems which are having resemblance to swarm intelligence, in their working behavior. In this paper, a brief description of the working of these algorithms is given.

Grid computing is recognized as one of the most powerful vehicles for high performance computing for data-intensive scientific applications. Grid is alternative to traditional distributed computing. It addresses issues such as resource discovery, heterogeneity, fault tolerance and task scheduling. Scheduling is the one of the current issue in the complex heterogeneous environment. Job scheduling is used to schedule the user jobs to appropriate resources in grid environment. The most simple and well known scheduling algorithm is min-min algorithm that is used to minimize the makespan but it has some disadvantages. To remove the disadvantages of the minmin in this paper we proposed an improved min-min algorithm which uses the features of Ant Colony Optimization.

This paper presents an in-depth study on the development and evaluation of an Elephant Herding Optimization (EHO) algorithm tailored to solve a set of standard benchmark functions, specifically f1 through f10. Drawing inspiration from the social behavior of elephant herds, the EHO algorithm employs strategies that mimic the leadership of matriarchs, the exploration conducted by male elephants, and the dynamic interplay between diversification and intensification during search. The study begins with a review of contemporary metaheuristic algorithms used in optimization tasks-such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Firefly Algorithm (FFA), Cuckoo Search (CS), and Tree Physiology Optimization (TPO)-and highlights existing performance gaps when solving complex benchmark functions. We then outline the design of the basic EHO method and introduce improved variants that incorporate novel individual updating strategies. These improvements include replacing suboptimal individuals with higher fitness particles, increasing the number of male elephants to enhance exploratory capabilities, and incorporating information from previous iterations to accelerate convergence. The proposed algorithm is benchmarked on ten classic test functions to comprehensively evaluate its convergence behavior, computational efficiency, and ability to search for global optima. Comparative analysis shows that the improved variants of EHO not only achieve faster convergence but also demonstrate enhanced robustness in terms of statistical consistency across multiple runs. The paper concludes with a discussion on the implications of these findings for both large-scale engineering applications and further research on nature-inspired optimization techniques.

In the last decade, the research community has devoted significant attention to wireless sensor networks (WSNs) because they contribute positively to some critical issues encountered in nature and even in industry. On the other hand, localization is one of the most important parts of WSN. Hence, the conception of an efficient method of localization has become a hot research topic. Lastly, it has been invented, a set of optimal positioning methods that make locate a node with low cost and give precise results. In our contribution, we investigate the source of imprecision in the distance vectorhop (DVHOP) localization algorithm. However, we found the last step of DVHOP caused an imprecision in the calculation. Consequently, our work was to replace this step, aiming to reach satisfactory precision. For that purpose, we created three improved versions of this algorithm by adopting two meta-heuristic (simulated annealing, particle swarm optimization) and Fmincon solver dedicated to optimization in the field of WSN node localization. The experimental results obtained in this work prove the efficiency of simulated annealing (SA)-DVHOP in terms of accuracy. Furthermore, the enhanced algorithm outperforms its opponents by varying the percentage of anchors and the number of nodes.

In the mobile environment, building applications submits to several constraints (i.e. the limited resources offered by the mobile devices and their execution state variability) which prevents their porting to heterogeneous devices available on the market. To deal with this challenge, we present a generic approach to compose mobile applications, using existing heterogeneous software entities, which can be adaptive to several heterogeneous devices mobile. It enables the smart and the dynamic composition of software entities independently from theirs implementation platforms. It also takes into account the specific features of the mobile device in which the desired application will be installed during the composition. This approach aims to provide mobile applications with contextaware contents adapted to the user’s requirements. Keywords—Composition, Reusing software entities, Heterogeneity, Mobile devices constraints.

Due the fact that Mobile Cloud Computing (MCC) is a recent paradigm, it lacks tools and frameworks to model its architecture. Existing ADLs (Architectural Description Languages) do not support modelling Mobile Cloud Architecture (MCA), since the latter encompasses more complicated and rich architectural features -including contextual informationthan traditional applications. In this paper we propose a Framework that allows: 1Describing mobile Cloud applications architecture with the Mobile Cloud ADL (MC-ADL). 2Provides context-aware middleware Smart Cloud Gate to ensure contextual configuration of the mobile cloud application at runtime and contextual evolution of Mobile Cloud Application's architecture.

Cold rolling is widely recognized as a key industrial process for enhancing the mechanical properties of materials, particularly hardness, through strain hardening. Despite its importance, accurately predicting the final hardness remains a challenge due to the inherently nonlinear nature of the deformation. While several studies have employed artificial neural networks to predict mechanical properties, architectural parameters still need to be investigated to understand their effects on network behavior and model performance, ultimately supporting the design of more effective architectures. This study investigates hyperparameter tuning in artificial neural networks trained using Resilient Backpropagation by evaluating the impact of varying number of hidden layers and neurons on the prediction accuracy of hardness in 70-30 brass specimens subjected to cold rolling. A dataset of 1000 input-output pairs, containing dimensional and hardness measurements from multiple rolling passes, was used to train and evaluate 819 artificial neural network architectures, each with a different configuration of 1 to 3 hidden layers and 4 to 12 neurons per layer. Each configuration was tested over 50 runs to reduce the influence of randomness and enhance result consistency. Enhancing the network depth from one to two hidden layers improved predictive performance. Architectures with two hidden layers achieved better performance metrics, faster convergence, and lower variation than single-layer networks. Introducing a third hidden layer did not yield meaningful improvements over two-hidden-layer architectures in terms of performance metrics. While the top three-layer model converged in fewer epochs, it required more computational time due to increased model complexity and weight elements.

Accurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combina...

Metaheuristic techniques have been recently used to counter the problems like slow convergence to global minima and network stagnancy in backpropagation neural network (BPNN) algorithm. Previously, a meta-heuristic search algorithm called Bat was proposed to train BPNN to achieve fast convergence in the neural network. Although, Bat-BP algorithm achieved fast convergence but it had a problem of oscillations in the gradient path, which can lead to sub-optimal solutions. In-order to remove oscillations in the BAT-BP algorithm, this paper proposed the addition of momentum coefficient to the weights update in the Bat-BP algorithm. The performance of the modified Bat-BP algorithm is compared with simple Bat-BP algorithm on XOR and OR datasets. The simulation results show that the convergence rate to global minimum in modified Bat-BP is highly enhanced and the oscillations are greatly reduced in the gradient path.

Optimization remains a cornerstone of modern engineering and computational intelligence, playing a vital role in the design, control, and allocation of limited resources across industries ranging from logistics to structural engineering. Traditional optimization methods, such as gradient-based and exact algorithms, often struggle with the nonlinear, multimodal, and constrained nature of real-world problems, necessitating the adoption of metaheuristic approaches. These biologically and physically inspired algorithms offer flexibility, scalability, and robustness in navigating complex search spaces. This study presents a systematic categorization of optimization problems-including combinatorial, continuous, constrained, and multiobjective classes-followed by a rigorous comparative analysis of nine prominent metaheuristics: Ant Colony Optimization (ACO), Lion Algorithm (LA), Cuckoo Search (CS), Grey Wolf Optimizer (GWO), Vibrating Particles System (VPS), Social Spider Optimization (SSO), Cat Swarm Optimization (CSO), Bat Algorithm (BA), and Artificial Bee Colony (ABC). The algorithms are evaluated across five representative benchmark problems: the Traveling Salesman Problem (TSP), Welded Beam Design (WBD), Pressure Vessel Design (PVD), Tension/Compression Spring Design (TSD), and the Knapsack Problem (KP). Key contributions include: 1)Domain-specific suitability analysis, revealing how algorithmic mechanisms align with problem structures. 2) Performance benchmarking under standardized conditions, highlighting convergence speed, solution quality, and constraint-handling efficacy. 3) Practical insights for practitioners on algorithm selection, hybridization potential, and adaptation challenges. Results demonstrate that no single algorithm dominates universally; instead, problem characteristics dictate optimal choices. For instance, ACO excels in discrete problems (TSP, KP), while GWO and BA outperform in continuous engineering designs (WBD, PVD). The study concludes with recommendations for future research, including dynamic parameter tuning, hybrid models, and real-world scalability assessments.

Metaheuristics are a category of optimization algorithms constructed to tackle complex real-world problems which traditional approaches struggle to address. This chapter analyzes the classification, application and evolution of metaheuristics algorithms in dealing with large-scale, NP-hard and nonlinear constraints. Metaheuristics, involving swarm intelligence, physics-based models, and evolutionary approaches, provide robust search framework by maintaining a balance between exploration and exploitation techniques. The discussion includes their contribution in biomedical image processing, finance, artificial intelligence and robotics. The study also investigates the computational problems of metaheuristics, focusing on parameter tuning, trade-offs in performance and process of hybridization with machine learn

This work introduces the symbiotic organisms search (SOS) evolutionary algorithm to the optimization of reservoir operation. Unlike the genetic algorithm (GA) and the water cycle algorithm (WCA) the SOS does not require specification of algorithmic parameters. The solution effectiveness of the GA, SOS, and WCA was assessed with a single-reservoir and a multi-reservoir optimization problem. The SOS proved superior to the GA and the WCA in optimizing the objective functions of the two reservoir systems. In the single reservoir problem, with global optimum value of 1.213, the SOS, GA, and WCA determined 1.240, 1.535, and 1.262 as the optimal solutions, respectively. The superiority of SOS was also verified in a hypothetical four-reservoir optimization problem. In this case, the GA, WCA, and SOS in their best performance among 10 solution runs converged to 97.46%, 99.56%, and 99.86% of the global optimal solution. Besides its better performance in approximating optima, the SOS avoided premature convergence and produced lower standard deviation about optima.

Nature is a rich source of inspiration, which has inspired many researchers in many ways. Nowadays, new algorithms have been developed by the inspiration from nature. The flower pollination algorithm is based on the characteristics of pollination process of flowers plants. Pollination is a natural biological process of mating in plants. In flowers, pollen is carried to stigma through some mechanisms that confirm a proper balance in the genetic creations of the species. Another nature inspired algorithmthe Bat algorithm is based on the echolocation behavior of bats. In this paper, the Flower pollination algorithm is compared with the basic Bat algorithm. We have tested these two algorithms on both unimodal and multimodal, low and high dimensional continuous functions. Simulation results suggest that the Flower pollination algorithm can perform much better than the Bat algorithm on the continuous optimization problems.

This article presents the study of frequency regulation of a solar energy based two area integrated power system equipped with multi-staged PID & conventional PID controller. To optimize the parameters of the projected multi-staged PID controllers, invasive weed optimization algorithm (IWOA) is employed. A disturbance to the extent of 0.01 p.u. is injected to the control area-1 to examine the effectiveness of the controllers in maintaining the system stability. Comparison of the results with respect to the magnitude of oscillations and settling time indicates that the proposed multi-staged PID controller outperforms the classical PID controller. The projected multi-staged PID controller optimised by the aforesaid algorithm (IWOA) helps in attaining nominal values of the tie-line power & frequency deviations during large parametric variation of the system and variation of system condition. Robustness of the proposed control strategy is established under various conditions such as (i)...