Bees Algorithms

The phrase 'Web 2.0'has been used as a popular label to describe web technologies with a focus on collaboration, interactivity, user-created content, information and knowledge sharing, and social connections. It indicates a transformation of the web environment from a one-way information delivery tool, to a multi-way information sharing platform. Its popularity as a catch-phrase has spawned other terms such as enterprise 2.0 wherein social media and supporting technologies are introduced into the corporate ...

Polymeric materials, being capable of high mouldability, usability of long lifetime up to 50 years and availability at low cost properties compared to metallic materials, are in demand but finite element-based design engineers have limited means in terms of the limited material data and mathematical models. In particular, in the analysis of products with complex geometry, the stresses and strains of various amounts formed in the product should be known and evaluated in terms of a precise design of the product to fulfil life expectancy. Due to time and cost constraints, experimental data cannot be available for all cases required in analysis, therefore, finite element method--based simulations are commonly used by design engineers. This is also computationally expensive and requires a simpler and more precise way to complete the design more realistically. In this study, the whole creep behaviour of polypropylene for all stresses were obtained with 10% accuracy errors by artificial neural networks trained using existing experimental test results of the materials for a particular working range. The artificial neural network model was trained with traditional as well as heuristic based methods. It is demonstrated that heuristically trained ANN models have provided much accurate and precise results, which are in line with 10% accuracy of experimental data.

Load Frequency Control (LFC) is designed to keep system frequency close to its nominal value. This study addresses the problem of frequency variations in a hybrid power system and provides theoretically grounded remedies. These techniques were used in a hybrid power system that combines a Diesel Engine Generator (DEG) with photovoltaic (PV) arrays. A soft optimization technique using the Bees Algorithm (BA) is employed to fine-tune the proposed fuzzy logic controller parameters, aiming for optimal control and dynamic system performance. Implementing a controller for fuzzy logic optimized by the BA, results in improved frequency response of the PV-DEG system, both during and after load disturbances. The suggested method's efficacy is evaluated using MATLAB-Simulink simulations and tests under various load conditions. The results highlight its enhanced performance, demonstrating its effectiveness as a valuable tool for addressing challenges in electrical power systems, particularly in load frequency management. A comparison between a conventional PID controller and a non-optimized fuzzy logic controller was conducted to show the effectiveness of the suggested controller. The results indicate that the Fuzzy Logic Controller tuned by the BA significantly outperforms the other methods.

Multiprocessor system often aims to minimize the schedule length of the submitted jobs. For this, efficient scheduling of the parallel tasks on multiprocessor system is required. As the scheduling is an NP-Hard problem, meta-heuristics are often applied for this. This work applies parallel particle swarm optimization technique for multiprocessor task scheduling. As the scheduler designed using parallel PSO itself can execute in parallel on the multiprocessor systems the convergence is faster. The proposed algorithm takes care of local as well as global convergence. The performance evaluation of the proposed model is done by simulation and the result is quite encouraging.

Resource-constrained project scheduling problem (RCPSP) is an important, but computationally hard problem. Particle swarm optimization (PSO) is a well-known and highly used meta-heuristics to solve such problems. In this work, a simple, effective and improved version of PSO i.e. adaptive-PSO (A-PSO) is proposed to solve the RCPSP. Conventional canonical PSO is improved at two points; during the particle's position and velocity updation, due to dependent activities in RCPSP, a high possibility arises for the particle to become invalid. To overcome this, an important operator named valid particle generator (VPG) is proposed and embedded into the PSO which converts an invalid particle into a valid particle effectively with the knowledge of the in-degree and out-degree of the activities depicted by the directed acyclic graph. Second, inertia weight (ω) that plays a significant role in the quick convergence of the PSO is adaptively tuned by considering the effects of fitness value, previous value of ω and iteration counter. Performance of the model is evaluated on the standard benchmark data of the RCPSP problem. Results show the effectiveness of the proposed model in comparison to other existing state of the art model that uses heuristics/meta-heuristics. The proposed model has the potential to be applied to other similar problems. Communicated by V. Loia.

Biped locomotion has attracted much attention in recent years. The most successful implemented methods in this area are based on two approaches, central pattern generator (CPG) and zero moment point (ZMP). Unfortunately, neither of these concepts can solely solve the movement challenge completely. In this study, we introduce a hybrid controller to combine the advantages of these methods. The proposed controller is based on two major approaches, CPG and ZMP. This hybrid controller is composed of a trajectory control system and a trajectory generator system. The trajectory control system applied to keep the robot stable uses ZMP as a real time control feedback. The trajectory generator system, which is composed of nonlinear oscillators, generates stable motions. The parameters of CPG are tuned by a new two-stage approach using differential evolution (DE) and bees algorithm (BA). Furthermore, performance of the proposed controller is verified using the robotic simulation software Webots.

Many public opinion surveys indicate that patients desire an open dialogue on spirituality/religion with their physicians. What role does spirituality play in the physician/patient relationship and in what contexts do these conversations arise? What do physicians think about the appropriateness of such discussions? Because the role of spirituality in clinical settings is so controversial, I wanted to learn from the experiences that physicians have had. My interviews with eight physicians helped me to establish their familiarity with spiritual assessment tools, to determine the contexts where spirituality is an important component of patient care, and to elucidate the methods these physicians use to best handle situations involving spirituality which are often characterized by complexity. Ultimately, I found that there is an incredible diversity to both the experiences and opinions held by physicians on the topic and that many physicians believe that spirituality has an important place in a clinical setting despite the substantial obstacles present that frequently prevent incorporating a spiritual component into a medical practice.

Artificial Intelligence (AI) control techniques involve the use of algorithms and models that mimic human intelligence and allow robots to learn from data, solve complex problems, and form opinions. These methods are extensively used in many different domains, such as data analysis, robotics, automation, and control systems. In recent years, the integration of renewable energy sources has led to the expansion and sophistication of power systems resulting in recurrent load frequency fluctuations.  The best control method to handle these fluctuations might not be obtained by strictly applying traditional or conventional control procedures. Hence, AI computing methods,  are increasingly being employed to address these challenges. This paper aims to provide a detailed review of various AI control techniques such as Fuzzy Logic Control (FLC), Teaching-Learning-Based Optimization (TLBO), The Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Bee Algorithms (BA), for load frequency management in power systems to enable other researchers explore the strength and weaknesses/ limitations of each technique for solving frequency control challenges in power systems. This review showed that there have been limited exploration of the Bees Algorithm in handling Load Frequency Control  in power systems.

Load Frequency Control (LFC) is designed to keep system frequency close to its nominal value. This study addresses the problem of frequency variations in a hybrid power system and provides theoretically grounded remedies. These techniques are used in a hybrid power system that combines a Diesel Engine Generator (DEG) with photovoltaic (PV) arrays. A soft optimization technique using the Bees Algorithm (BA) is employed to fine-tune the proposed fuzzy logic controller parameters for LFC, aiming for optimal control and dynamic system performance. Implementing a controller for fuzzy logic optimized by the BA, results in improved frequency response of the PV-DEG system, both during and after load disturbances. The effectiveness of the proposed method is assessed through MATLAB-Simulink simulations and tests under various load conditions. The results highlight its enhanced performance, demonstrating its effectiveness as a valuable tool for addressing challenges in electrical power systems, particularly in load frequency management. A comparison between a conventional PID controller and a simple fuzzy logic controller was done to show how effective the suggested controller is. The results indicate that the Fuzzy Logic Controller tuned by the BA significantly outperforms the other methods.

In the paper, authors analyze information and communication technology (ICT) for planning and optimization of transport routes with time windows. This analysis helps to choose the method for solving a vehicle routing problem with time windows (VRPTW) with optimal result for decision-maker, as well as determine the influence of the internal parameters of the selected method on the result of its application. Currently, there are several well-known methods and algorithms in ICT for planning and optimization of transport routes with time windows, in particular: saving and sweeping algorithms, ant colony optimization (ACO) method, artificial bee colony (ABC) method, etc. The result of the analysis showed that: a) the speed and quality of the search for solutions can be improved by adjusting internal parameters of researched methods, b) depending on the size and specificity of the in-coming data, different methods can be more or less suitable. In this paper, the authors discussed the features of using the ACO method and the ABC method to solve VRPTW and influence of their application on the optimality of the results. Simulation results show the need and feasibility of using ICT in VRPTW.

A generalized nonlinear model is formulated for the dynamic analysis of model 9 DOF road vehicle with active suspension systems. The model incorporates a nonlinear suspension spring and nonlinear damping force for active dampers and so, the linear model is formulated similar to Bouzara model. The active dampers are characterized by force generators in accordance with the control laws based upon suspension mass velocity. So, This paper reports on an investigation to determine the spring and damper settings that will ensure optimal ride comfort and road holding road vehicle, on different road profiles and at different speeds for linear and nonlinear vehicle models. The extent to which the ride comfort optimal suspension settings vary for roads of different roughness and varying speeds and the levels of ride comfort and road holding that can be achieved, are addressed. The issues of the best objective function to be used when optimizing and if a single road profile and speed can be used as representative conditions for ride comfort optimization of passive, semi-active and active suspensions, are dealt with. Optimization is performed with the Bees algorithm on a vibration model 9 degree of freedom for speeds ranging from 30 to 90 km/h. It is found that optimizing for a combined driver plus rear passenger seat weighted root mean square vertical acceleration rather than using driver or passenger values only, returns the best results.

Cloud computing is an emerging concept that makes better use of a large number of distributed resources. The most significant issue that affects the cloud computing environment is resource provisioning. Better performance in the shortest amount of time is an important goal in resource provisioning. Create the best solution for dynamically provisioning resources in the shortest time possible. This paper aims to perform resource provisioning with an optimal performance solution in the shortest time. Hybridization of two Meta-heuristics techniques, such as HSMOSA (Hybrid Spider Monkey Optimization with Simulated Annealing), is proposed in resource provisioning for cloud environment. Finds the global and local value using Spider Monkey Optimization's (SMO) social behavior and then utilizes Simulated Annealing (SA) to search around the global value in each iteration. As a result, the proposed approach aids in enhancing their chances of improving their position. The CloudSimPlus Simulator is used to test the proposed approach. The fitness value, execution time, throughput, mean, and standard deviation of the proposed method were calculated over various tasks and execution iterations. These performance metrics are compared with the PSO-SA algorithm. Simulation results validate the better working of the proposed HSMOSA algorithm with minimum time compared to the PSO-SA algorithm.

Exploration, the process of visiting a new region in a search space, and exploitation, the process of searching
in the neighborhood of previously visited regions, are two centerpieces of any metaheuristic algorithm. It is
a common belief that good results can be obtained only if there is a good balance between exploration and
exploitation. Hence, there is an urgent need to control the balance between exploration and exploitation in
a direct manner. But, currently, direct measures of exploration and exploitation are almost non-existent, and
researchers rely on indirect measures of exploration and exploitation, such as diversity, entropy, and fitness
improvements. To remedy this situation, in this paper, a novel direct measure of exploration and exploitation is
proposed that is based on attraction basins — parts of a search space where each part has its own point called
an attractor, to which neighboring points tend to evolve. Each search point can be associated with a particular
attraction basin. If a newly generated search point belongs to the same attraction basin as its parent then the
search process is identified as exploitation, otherwise as exploration. In this paper, a new technique to compute
attraction basins is presented, as well as a novel direct measure (𝐸𝑥𝑝𝐵𝑎𝑠) of exploration and exploitation based
on attraction basins. On the selected set of unimodal and multimodal optimization problems it is shown that the
newly proposed direct measure of exploration and exploitation is more accurate than our previously proposed
direct measure (𝐸𝑥𝑝𝐷𝑖𝑠𝑡), as well as the common indirect measure based on diversity (Diversity).

In this paper, we propose a novel efficient model based on Bees Algorithm (BA) for the Resource-Constrained Project Scheduling Problem (RCPSP). The studied RCPSP is a NP-hard combinatorial optimization problem which involves resource, precedence, and temporal constraints. It has been applied to many applications. The main objective is to minimize the expected makespan of the project. The proposed model, named Enhanced Discrete Bees Algorithm (EDBA), iteratively solves the RCPSP by utilizing intelligent foraging behaviors of honey bees. The potential solution is represented by the multidimensional bee, where the activity list representation (AL) is considered. This projection involves using the Serial Schedule Generation Scheme (SSGS) as decoding procedure to construct the active schedules. In addition, the conventional local search of the basic BA is replaced by a neighboring technique, based on the swap operator, which takes into account the specificity of the solution space of proj...

This chapter addresses an approach to generate 3D gait for humanoid robots. The proposed method considers gait generation matter as optimization problem with constraints. Firstly, trigonometric function is used to produce trial gait data for conducting simulation. By collecting the result, we build an approximation model to predict final status of the robot in locomotion, and construct optimization problem with constraints. In next step, we apply an improve differential evolution algorithm with Gauss distribution for solving optimization problem and achieve better gait data for the robot. This approach is validated using Kondo robot in a simulated dynamic environment. The 3D gait of the robot is compared to human in walk.

This chapter addresses an approach to generate 3D gait for humanoid robots. The proposed method considers gait generation matter as optimization problem with constraints. Firstly, trigonometric function is used to produce trial gait data for conducting simulation. By collecting the result, we build an approximation model to predict final status of the robot in locomotion, and construct optimization problem with constraints. In next step, we apply an improve differential evolution algorithm with Gauss distribution for solving optimization problem and achieve better gait data for the robot. This approach is validated using Kondo robot in a simulated dynamic environment. The 3D gait of the robot is compared to human in walk.

In this study, during the in-factory logistics process, the optimization of the route for material transport, including raw materials, semi-finished products, ready-made parts, and auxiliary materials, has been achieved through the application of the Bees Algorithm. It is extremely important to transport all kinds of materials from the warehouse in the factory to the production line. In factories whose areas are growing day by day, problems such as inefficient use of material carriers, long distances they travel and traffic they cause inside the factory are frequently observed. Whether the material transport is autonomous or operator-driven, optimal route planning can address these challenges, leading to increased operational efficiency, improved production capabilities, reduced downtime, and cost savings. Additionally, it aids in planning business operations sequence, ensuring timely acquisition of materials in the right quantity and quality. To achieve these objectives, a mathematical model for the route of the material carrier in a 150,000 square meter factory has been developed, and the Bees Algorithm, a metaheuristic algorithm, has been employed to minimize the travel distance. The results demonstrate a significant reduction in the material carrier's travel distance, presented numerically and graphically in tables.

A new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the food foraging behaviour of swarms of honey bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search and can be used for both combinatorial optimisation and functional optimisation. This paper focuses on the latter. Following a description of the algorithm, the paper gives the results obtained for a number of benchmark problems demonstrating the efficiency and robustness of the new algorithm.

An improved version of the Bees Algorithm is proposed for solving dynamic optimisation problems. This new formulation of the Bees Algorithm includes new search operators, and a new selection procedure that enhances the survival probability of newly formed individuals. The proposed algorithm is tested on six benchmark dynamic optimisation problems. The benchmark problems include minimisation and maximisation tasks of different levels of complexity. For all the problems, the Bees Algorithm finds very satisfactory optima. The very small standard deviation of the results proves the high reliability of the proposed technique. Experimental tests show that the Bees Algorithm outperforms the state-of-the-art Ant Colony Optimisation algorithm. The Bees Algorithm improves also the best results published in the literature for the six benchmark problems.

Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA is that it requires long computational time as well as numerous computational processes to obtain a good solution, especially in more complicated issues. This approach does not guarantee any optimum solutions for the problem mainly because of lack of accuracy. To solve this issue, the local search in the BA is investigated by Simple swap, 2-Opt and 3-Opt were proposed as Massudi methods for Bees Algorithm Feature Selection (BAFS). In this study, the proposed extension methods is 4-Opt as search neighbourhood is presented. This proposal was implemented and comprehensively compares and analyse their performances with respect to accuracy and time. Furthermore, in this study the feature selection algorithm is implemented and tested using most popular dataset from Machine Learning Repository (UCI). The obtained results from experimental work confirmed that the proposed extension of the search neighbourhood including 4-Opt approach has provided better accuracy with suitable time than the Massudi methods.

The Bees Algorithm (BA) has been applied for finding the best possible subset features of a dataset. However, the main issue of the BA for feature selection is that it requires long computational time. This is due to the nature of BA combination search approach that exploits neighborhoods with random explorative. This situation creates unwanted suboptimum solution(s) leading to the lack of accuracy and longer processing time. A set of different local neighborhood search extension and their combination approaches have been proposed, including Simple-swap, 2-Opt, 3-Opt, and 4-Opt. The performance of the proposed mechanism was compared and analyzed using benchmark dataset. The results from experimental work confirmed that the proposed approach provides better accuracy with suitable time.

The optimization problems have been widely attacked in the area of evolutionary computation, this has been due mainly to the kindness they have shown to solve these problems. This chapter addresses the solution to the problem of optimizing the space within the vehicle to distribute purified water. To solve this problem using a cultural algorithm which helps determine the appropriate charge that provides the best value for the company of this kind of services to the customers of the product. This is done taking into account the space that is available within the vehicle and the different presentations that are distributed. In turn, this study helps us to understand the behavior of (Cultural Algorithms) with respect to the issues raised in the optimization of space vehicle for distributing purified water. In addition prepare a comparative analysis of the results obtained with respect to another method of solving optimization space, and finally describe an extended Partially Dynamic Ve...

In this paper, an integer quadratic programming model is proposed for workload balancing in the examination job assignment problem. The proposed model can be classified as part of the 'Generalised Assignment Problem' or GAP, which is known as an NP-hard problem. To solve the proposed model, a relatively new member of the swarm intelligence family known as the Bees Algorithm (BA) is used. The proposed mathematical model is also solved for nine sets of randomly generated test problems with a very well-known classical optimiser (CPLEX 10.1-MIQP) to present the competitive performance of the BA algorithm. The obtained results show that the proposed BA algorithm is able to generate better solutions with much shorter computational times.

In this paper, an integer quadratic programming model is proposed for workload balancing in the examination job assignment problem. The proposed model can be classified as part of the 'Generalised Assignment Problem' or GAP, which is known as an NP-hard problem. To solve the proposed model, a relatively new member of the swarm intelligence family known as the Bees Algorithm (BA) is used. The proposed mathematical model is also solved for nine sets of randomly generated test problems with a very well-known classical optimiser (CPLEX 10.1-MIQP) to present the competitive performance of the BA algorithm. The obtained results show that the proposed BA algorithm is able to generate better solutions with much shorter computational times.

This paper describes a new application of the Bees Algorithm to the optimisation of a Support Vector Machine (SVM) for the problem of classifying defects in plywood. The algorithm, which is a swarm-based algorithm inspired by the food foraging behaviour of honey bees, was also employed to select the components making up the feature vectors to be presented to the SVM. The objective of the work was to find the best combination of SVM parameters and data features to maximise defect classification accuracy. The paper presents the results obtained to demonstrate the strengths of the Bees Algorithm as an optimisation tool.

Tasks allocation is an important step for obtaining high performance in distributed computing system (DCS). This article attempts to develop a mathematical model for allocating the tasks to the processors in order to achieve optimal cost and optimal reliability of the system. The proposed model has been divided into two stages. Stage-I, makes the ‘n' clusters of set of ‘m' tasks by using k-means clustering technique. To use the k-means clustering techniques, the inter-task communication costs have been modified in such a way that highly communicated tasks are clustered together to minimize the communication costs between tasks. Stage-II, allocates the ‘n' clusters of tasks onto ‘n' processors to minimize the system cost. To design the mathematical model, executions costs and inter tasks communication costs have been taken in the form of matrices. To test the performance of the proposed model, many examples are considered from different research papers and results of ...

Solving a classical vehicle routing problem (VRP) by exact methods presents many difficulties for large dimension problem. Consequently, in multi-objective framework, heuristic or metaheuristic methods are required. Due to particular VRP structure, it seems that a dedicated heuristic is more suitable than a metaheuristic. The aim of this article is to collapse different heuristics solving classical VRP and adapt them for to solve the multi-objective vehicle routing problem (MOVRP). The so-called Cobweb Algorithm simulates spider's behavior when weaving cobweb. This paper presents the algorithm, a didactic example, concluding remarks and way for further researches.

The problem of determining vehicle routes in logistics has a role Which on pIt isnting bagi company to reduce transportation costs and late fees. Time window constraints are very common in current distribution processes. This research focuses on optimizing fuel costs and penalties. In this study the authors used the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW is one of the most tackled transportation problems in real world situations. Artificial Bee Colony Algorithm (ABC) and Camel Algorithm (CA) will be used in the research. Study This is Focused on optimizing fuel costs and penalties. Furthermore, the two algorithms will be compared which of the two algorithms is the most optimal for solving the route determination problem in VRPTW. Numerical testing will be carried out for the proposed algorithm, as well as conducting experiments on several parameters to determine the effect of parameters for fuel costs and penalties.

Cloud computing and Internet of Things (IoT) are computing technologies that provide services to consumers and businesses, allowing organizations to become more agile and flexible. The potential business values that cloud consumers can achieve depend a lot on the quality of service in the provided cloud services. Therefore, ensuring the quality of service through service-level agreements (SLA) for such cloud-based services is crucial for both the service providers and service consumers. As SLA is critical for cloud deployments and wider adoption of cloud services, the management of SLA in cloud and IoT has thus become an important and essential issue. In this paper we provide an understanding of the current status and maturity level of SLA management in industrial IoT and academic efforts in this field. We also conduct a preliminary survey of current research on SLA management in order to identify open challenges and gaps that need to be addressed in future research directions. In particular, we investigate how to provide useful SLA management support adapted to the maturity level and current industrial practices, and shorten the gap between academia and industry.

The Transmission Expansion Planning (TEP) entails to determine all the changes needed in the electric transmission system infrastructure in order to allow the balance between the projected demand and the power supply, at minimum investment and operational costs. In some type of TEP studies, the DC model is used for the medium and long term time frame, while the AC model is used for the short term. This chapter proposes a load shedding based TEP formulation using the DC and AC model, and four Particle Swarm Optimization (PSO) based algorithms applied to the TEP problem: Global PSO, Local PSO, Evolutionary PSO, and Adaptive PSO. Comparisons among these PSO variants in terms of robustness, quality of the solution, and number of function evaluations are carried out. Tests, detailed analysis, guidelines, and particularities are shown in order to apply the PSO techniques for realistic systems.

This paper proposes a new population-based simplex method for continuous function optimization. The proposed method, called Adaptive Population-based Simplex (APS), is inspired by the Low-Dimensional Simplex Evolution (LDSE) method. LDSE is a recent optimization method, which uses the reflection and contraction steps of the Nelder-Mead Simplex method. Like LDSE, APS uses a population from which different simplexes are selected. In addition, a local search is performed using a hyper-sphere generated around the best individual in a simplex. APS is a tuning-free approach, it is easy to code and easy to understand. APS is compared with five state-of-the-art approaches on 23 functions where five of them are quasi-real-world problems. The experimental results show that APS generally performs better than the other methods on the test functions. In addition, a scalability study has been conducted and the results show that APS can work well with relatively high-dimensional problems.

Abstract—This article deals with a usage of artificial intelligent techniques in humanoid robotics. The focus is on social robotics and how to use the interactive evolution for robotic dance system. In this work the definition, principles and basic features of Interactive Evolutionary Computation are described and followed by an overview of their research and applications. This technique optimizes systems based on subjective human evaluation. The algorithm is applied to a system of design of robotic dance, in which the evolutionary ...

In this study, a flexible robot arm model and the design of its controller are introduced. The robot arm consists of a single flexible link. It is desired to control the circular position of the robot arm and the vibration of the tip point. Cascade proportional derivative controller was used to control the position and reduce the tip vibration. Controller gains were found using The Bees Algorithm. The weighted function of system responses such as settling time, maximum overshoot and steady-state error is used as a performance criterion while searching for the best parameters. In addition, controller gains were obtained with the genetic algorithm to evaluate the working performance of The Bees Algorithm. It has been observed that the Cascade PD controller, whose gains are optimized by The Bees Algorithm, successfully controls the flexible robot arm system and reduces the vibration of the tip point.

Bu çalışmada, üç serbestlik dereceli (RRR) düzlemsel bir çizim robotuna ait uç işlevcinin konum kontrolüne yönelik kontrolcü tasarımı ve arıların sezgisel davranışlarından esinlenilerek geliştirilmiş sürü tabanlı Arı Algoritması (AA) ile optimizasyonu gerçekleştirilmiştir. Öncelikle sistemin matematiksel modellemesi elde edilmiş ve ileri kinematik hesapları yapılmıştır. Analitik hesaplamaların yanı sıra, sistemin katı modeli üzerinden sistemin çok kütleli dinamik modellemesi ve PID kontrolcü tasarımı Matlab/Simulink ortamında yapılmıştır. Uç işlevcinin yörünge hatasını en aza indirmek ve optimal PID kontrolcü elde etmek amacıyla, kontrolcü kazançları (Kp-oransal, Ki-integral, Kd-türevsel) AA optimizasyonu sonucu elde edilmiştir. Ayrıca optimize edilmiş PID parametreleri kullanılarak 8 (sekiz) ve kare çizimleri yaptırılmıştır. Yapılan çizimler ile referans çizim arasındaki karşılaştırma 11 farklı hata kriteri göz önün bulundurularak elde edilen hata değerleri esas alınarak yapılmıştı...

In this paper, we propose two slightly different models based on Bees Algorithm (BA) for the Resource-Constrained Project Scheduling Problem (RCPSP). The studied RCPSP is a NP-hard combinatorial optimization problem which involves resource, precedence, and temporal constraints. It has been applied to many applications. The main objective is to minimize the expected makespan of the project. The proposed Enhanced Discrete Bees Algorithms iteratively solve the RCPSP by utilizing intelligent foraging behaviours of honey bees. The potential solution is represented by the multidimensional bee, where the Activity List representation (AL) is considered. This projection involves using the Serial Schedule Generation Scheme (SSGS) as decoding procedure to construct the active schedules. In addition, the conventional local search of the basic BA is replaced by a neighbouring technique, based on the Swap operator, which takes into account the specificity of the solution space of project scheduling problems and reduces the number of parameters to be tuned. As second variant, the Negative Selection is embedded into our approach to overcome its drawback. The proposed models are tested on well-known benchmark problem instance sets from Project Scheduling Problem Library (PSPLIB) and compared with other approaches from the literature. The promising computational results reveal the effectiveness of the proposed approaches for solving the RCPSP problems of various scales.

Themainpurposeofthisarticleistodemonstratehowevolutionstrategyoptimizerscanbeimproved byincorporatinganefficienthybridizationschemewithrestartstrategyinordertojumpoutoflocal solutionregions.Theauthorsproposeahybrid(μ,λ)ES-NMalgorithmbasedontheNelder-Mead (NM)simplexsearchmethodandevolutionstrategyalgorithm(ES)forunconstrainedoptimization. Atfirst,amodifiedNM,calledAdaptiveNelder-Mead(ANM)isusedthatexhibitsbetterproperties thanstandardNMandself-adaptiveevolutionstrategyalgorithmisappliedforbetterperformance,in additiontoanewcontractioncriterionisproposedinthiswork.(μ,λ)ES-NMisbalancingbetween theglobalexplorationoftheevolutionstrategyalgorithmandthedeepexploitationoftheNelder-Meadmethod.Theexperimentresultsshowtheefficiencyofthenewalgorithmanditsabilityto solveoptimizationproblemsintheperformanceofaccuracy,robustness,andadaptability.

Themainpurposeofthisarticleistodemonstratehowevolutionstrategyoptimizerscanbeimproved byincorporatinganefficienthybridizationschemewithrestartstrategyinordertojumpoutoflocal solutionregions.Theauthorsproposeahybrid(μ,λ)ES-NMalgorithmbasedontheNelder-Mead (NM)simplexsearchmethodandevolutionstrategyalgorithm(ES)forunconstrainedoptimization. Atfirst,amodifiedNM,calledAdaptiveNelder-Mead(ANM)isusedthatexhibitsbetterproperties thanstandardNMandself-adaptiveevolutionstrategyalgorithmisappliedforbetterperformance,in additiontoanewcontractioncriterionisproposedinthiswork.(μ,λ)ES-NMisbalancingbetween theglobalexplorationoftheevolutionstrategyalgorithmandthedeepexploitationoftheNelder-Meadmethod.Theexperimentresultsshowtheefficiencyofthenewalgorithmanditsabilityto solveoptimizationproblemsintheperformanceofaccuracy,robustness,andadaptability.

The aim of this work is to study the effect of hybridization on the performance of the Artificial Bee Colony (ABC) as arecently introduced metaheuristic for solving Resource Constrained Project Scheduling Problem (RCPSP). For thispurpose the ABC is combined with the Genetic Algorithm (GA). At the initial time, the algorithm generates a set ofschedules randomly. The initial solution is evaluated against constraints and the infeasible solutions are resolved tofeasible ones. Then, the initial schedules will be improved iteratively using hybrid method until termination condition ismet. The proposed method works by interleaving the ABC and GA search processes. The GA method updates schedulesby considering the best solution found by the ABC approach. Next the ABC approach picks the solutions found by GAsearch. A new approach is used by the algorithm to maintain the priorities of the activities in feasible ranges. Theperformance of the proposed algorithm is compared against a set of state-...

Wheeled robot locomotion is not adapted to many human areas, e.g. areas littered by many obstacles, while humanoid robots can be adapted to human environments since they can attain desirable posture and avoid obstacles of different shapes. Due to the huge controller design space as well as inherently nonlinear dynamics of walking, controlling the balance of humanoid walking is a very challenging task. In addition to the walking balance issue, in order to use humanoid walking in daily life tasks, humanoids should also be capable of walking in an optimized manner, e.g. energy efficient and high speed. There is a large number of approaches to generate humanoid walking, which are mainly divided into model-based and model-free approaches. However, there is still not a clear methodology to address the issue of generating an optimized omnidirectional walking in the sense of creating a fast and energy efficient walk. This thesis is intended to develop biped locomotion approaches in order to generate an optimized omnidirectional walking. Model-free approaches based on Truncated Fourier Series (TFS) and model-based approaches based on Zero Moment Point (ZMP) are studied to generate optimized walking. The TFS based approach is improved to generate three-dimensional walking motions using all legs degrees of freedom. This improvement enables the robot to perform turnin-place and to walk faster when compared with the previously presented TFS based approaches. ZMP-based approaches are also presented which can generate omnidirectional walking using the cart-table model. An analytical approach is presented to solve the differential equations of the cart-table model allowing the robot to change the double support period during the walk. The cart-table cannot model hip height movement during walking. However, in this thesis, a ZMP-based approach is presented which can model an omnidirectional walking with variable hip height. The hip height movement is generated using Fourier series and Central Pattern Generators (CPGs) that had been used in model-free walking approaches. Gait optimization approaches are applied to the proposed ZMP-based walk engine with variable hip. This enables the robot to walk faster and also to walk with higher energy efficiency in comparison with the walking generated using the regular cart-table model. This work was funded by the Portuguese Foundation for Science and Technology (FCT) under the grant SFRH / BD / 66597 / 2009, gratefully acknowledged. I offer my special thanks to my supervisor Prof. Dr. Luis Paulo Reis for his guidance and generous support throughout this work. Also, I would like to express my sincere gratitude to my supervisor Prof. Dr. Nuno Lau for his inspiration and excellent guidance. I would also thank the Institute of Electronics and Telematics Engineering of Aveiro (IEETA) for its supporting of my work in the last year of my Ph.D. in the concept of the grant PEst-OE/EEI/UI0127/2014. Moreover, I acknowledge the (current and former) members of both the Artificial Intelligence and Computer Science Laboratory (LIACC) and IEETA, in particular Rui Ferreira, Abbas Abdolmaleki, and Edgar Domingues, whose scientific discussions were certainly a plus. Concerning the non-academic side of my life, my respectful thanks are to my loving parents Abdoulreza Shafii and Vajiheh Rezagholinejad for their constant encouragement and support in all stages of my studies. Last but certainly not least, I would like to thank my beloved wife Shirin. She made a fundamental contribution to the development of this work. I managed to finish this thesis mainly because of her excellent support. vii "Your hand can seize today, but not tomorrow; and thoughts of our tomorrow are nothing but desire. Don't waste this breath, since the rest of your life won't last forever."

This paper is considered with a non-linear suspension design for half vehicle model by using particle swarm optimization technique. To analyze the ride comfort, a five-degree of freedom system is built, and it is integrated with the Particle Swarm Optimization (PSO) for optimizing the vehicle vibrations. A multi-objective function is proposed as the sum of the minimum seat and vehicle body acceleration, the minimum suspension deflection and the constraints and the design variables of the optimization problem are selected as the spring and damping coefficients of the front and rear suspension and the non-linear spring and the linear damping coefficients of the seat. The simulations are carried out and the results are compared with the non-optimized values. It is demonstrated that the vehicle vibration is decreased significantly with the help of the optimum values of the suspension parameters.

Humanoid robots are made to resemble humans but their locomotion abilities are far from ours in terms of agility and versatility. When humans walk on complex terrains, or face external disturbances, they combine a set of strategies, unconsciously and efficiently, to regain stability. This paper tackles the problem of developing a robust omnidirectional walking framework, which is able to generate versatile and agile locomotion on complex terrains. The Linear Inverted Pendulum Model and Central Pattern Generator concepts are used to develop a closed-loop walk engine, which is then combined with a reinforcement learning module. This module learns to regulate the walk engine parameters adaptively, and generates residuals to adjust the robot's target joint positions (residual physics). Additionally, we propose a proximal symmetry loss function to increase the sample efficiency of the Proximal Policy Optimization algorithm, by leveraging model symmetries and the trust region concept. The effectiveness of the proposed framework was demonstrated and evaluated across a set of challenging simulation scenarios. The robot was able to generalize what it learned in unforeseen circumstances, displaying human-like locomotion skills, even in the presence of noise and external pushes.

This paper proposes a modular framework to generate robust biped locomotion using a tight coupling between an analytical walking approach and deep reinforcement learning. This framework is composed of six main modules which are hierarchically connected to reduce the overall complexity and increase its flexibility. The core of this framework is a specific dynamics model which abstracts a humanoid's dynamics model into two masses for modeling upper and lower body. This dynamics model is used to design an adaptive reference trajectories planner and an optimal controller which are fully parametric. Furthermore, a learning framework is developed based on Genetic Algorithm (GA) and Proximal Policy Optimization (PPO) to find the optimum parameters and to learn how to improve the stability of the robot by moving the arms and changing its center of mass (COM) height. A set of simulations are performed to validate the performance of the framework using the official RoboCup 3D League simulation environment. The results validate the performance of the framework, not only in creating a fast and stable gait but also in learning to improve the upper body efficiency.

Parameter identification is an important research topic with a variety of applications in industrial and environmental problems. Usually, a functional has to be minimized in conjunction with parameter identification; thus, there is a certain similarity between the parameter identification and optimization. A number of rigorous and efficient algorithms for optimization problems were developed in recent decades for the case of a convex functional. In the case of a non-convex functional, the metaheuristic algorithms dominate. This paper discusses an optimization method called modified bee colony algorithm (MBC), which is a modification of the standard bees algorithm (SBA). The SBA is inspired by a particular intelligent behavior of honeybee swarms. The algorithm is adapted for the parameter identification of reaction-dominated pore-scale transport when a non-convex functional has to be minimized. The algorithm is first checked by solving a few benchmark problems, namely finding the min...

We address in this paper a delivery process with time requirements in the supply chain stated as follows: orders launched from customers are centralised and assigned to firms' depots for the delivery process. The consideration of a depot and a set of customers belonging to different firms are seen as a VRPTW that serves n customers using a subset of vehicles. We implement in this paper a DSS that handles the delivering activity in the supply chain. The DSS embeds a GRASP and genetic components for generating promising solution in a concurrently run time. In addition we proposed a V NSH-based hyper-heuristic that tries to select the most accurate neighbourhood at each iteration to apply according to its previous performance. Simulation results are conducted on Solomon's benchmarks. The DSS recorded very competitive results regarding state-of-the-art approaches.

Honey bee colonies depend on pollen and nectar from flowers for their feed. The act of searching for this flowers by the bees is called foraging. The foraging behaviour of bees depends on the profitability of nectar and pollen sources as well as the needs of the colony. This behaviour is modeled into an algorithm called Foraging Bee Optimization Algorithm (FBA). After initialization, the algorithm loops through three phases based on bees’ nature foraging behaviour called the 3W: Waggle, Work, and Withdraw. A large number of flowers are initialized randomly in the problem space. During the waggle phase, bees are recruited to patch with profitable nectar sources. In the work phase, new flowers are discovered and memorized by bees. In the withdraw phase bees eliminate unprofitable flowers and recalibrate for recruitment. The proposed FBA is tested on three unimodal and twelve multimodal benchmark. The result is compared with two state-of-the-art natured-inspired optimization algorithm.