A Study Of Hybrid Particle Swarm Optimization And Its Applications

Particle swarm optimization(PSO)is a stochastic population-based algorithm motivated by intelligent collective behavior of birds.The performance of the PSO algorithm highly depends on choosing appropriate parameters.Inertia weight is a parameter of this algorithm which was used to a balance between the exploration and exploitation characteristics of PSO.PSO should have strong yet balanced exploration and exploitation capabilities to enhance its performance.The advantages of PSO algorithm include fast convergence toward the global optimum,easy implementation and few parameters to adjust.Its effective searching strategy makes it a potential method for solving different optimization problems in a wide variety of applications.PSO has been widely used in many fields such as power system optimization,process control,dynamic optimization,adaptive control and electromagnetic optimization.Although PSO has shown good performance in solving many optimization problems,it suffers from the problem of premature convergence like most of the stochastic search techniques,particularly in multimodal optimization problems.Although noticeable progress and fruitful achievements have been attained,successfully balancing the exploration and exploitation capabilities of PSO to determine high-quality solutions.This thesis will combine local search with PSO to balance exploration and exploitation capabilities,and apply the improved proposed method to many optimization problems and real-world problems.The main works of this thesis are listed as follows:(1)An improved self-inertia weight adaptive particle swarm optimization with a gradient-based local search strategy(SIW-APSO-LS)is proposed.This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation with the gradient-based local search strategy.The self-inertia weight adaptive particle swarm optimization(SIW-APSO)is used to search the solution.The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions.The gradient-based local search focuses on the exploitation ability because it performs an accurate search following SIW-APSO.Experimental results have verified that the proposed algorithm performed well compared with other PSO variants on a suite of benchmark optimization functions.(2)A new hybrid population-based algorithm is proposed with the combination of dynamic multi swam particle swarm optimization and gravitational search algorithm(GSADMSPSO).The proposed algorithm divides the main population of masses into smaller sub-swarms and also stabilizing them by presenting new neighborhood strategy.At this time,by adopting the global search ability of proposed algorithm,each agent(particle)improves the position and velocity.The main idea is to integrate the ability of GSA with the DMSPSO to enhance the performance of exploration and exploitation of a proposed algorithm.In order to evaluate the competences of the proposed algorithm benchmark functions are employed.The experimental results have been confirmed a better performance of GSADMSPSO as compared to the other gravitational and PSO variants in terms of fitness rate.(3)Feature selection is an important task to improve prediction accuracy of classifiers and to decrease the problem size.Numerous methodologies have been presented to achieve feature selection using metaheuristic algorithms.In this study,an improved self-hybrid individual inertia weight adaptive particle swarm optimization with local search and combined with C4.5 classifiers for feature selection algorithm is proposed.In this proposed algorithm,we combine the gradient base local search with its capacity of helping to explore the feature space and improved self-hybrid individual inertia weight adaptive particle swarm optimization with its ability to converge to the best global solution in the search space.Experimental results have verified that the SIW-APSO-LS performed well compared with other current state of art feature selection techniques on a suit of 16 standard data sets obtained from the UCI repository.(4)In this study,GSADMSPSO is employed as new training methods for FNNs in order to examine the efficiencies of the algorithm in decreasing the problems of trapping in local minima and the slow convergence rate of current evolutionary learning algorithms.The performance of the proposed algorithm is compared with GSA and its variants on different suites of well-known benchmark test functions.The experimental results show that proposed algorithm outperforms to other variants training FNNs in terms of converging speed and avoiding local minima.