Abstract
Particle Swarm Optimization (PSO) algorithm is a population-based strong stochastic search strategy empowered from the inherent way of the bee swarm or animal herds for seeking their foods. Consequently, flexibility for the numerical experimentation, PSO has been used to resolve diverse kind of optimization problems. PSO is much of the time caught in local optima in the meantime taking care of the complex real-world problems.Considering this, a novel modified PSO is introduced by proposing a chi square mutation method. The main functionality of mutation operator in PSO is quick convergence and escapes from the local minima. Population initialization plays a critical role in meta-heuristic algorithm. Moreover, in this work, to improve the convergence, rather applying random distribution for initialization, two quasi random sequences Halton and Sobol have been applied and properly joined with chi-square mutated PSO (Chi-Square PSO) algorithm. The promising experimental result suggests the superiority of the proposed technique. The results present foresight that how the proposed mutation operator influences on the value of cost function and divergence. The proposed mutated strategy is applied for eight (8) benchmark functions extensively used in the literature. The simulation results verify that Chi-Square PSO provide efficient results over other tested algorithms implemented for the function optimization.
Highlights
The term “swarm intelligence” is practiced as to explain the algorithm and distributed problem solvers, motivated by the common actions of colonies of insects and other animal groups
In order to measure the execution of the proposed chi-square particle swarm optimization (PSO) algorithm, a group of benchmark functions has been utilized to do the comparison with many other improved PSO techniques with traditional PSO, Adaptive PSO and different initialization techniques
In order to check the performance of each technique, all algorithms tested for 30 runs
Summary
The term “swarm intelligence” is practiced as to explain the algorithm and distributed problem solvers, motivated by the common actions of colonies of insects and other animal groups. Other evolutionary algorithms (EAs), the particle swarm optimization (PSO) technique is a population based metaheuristic search approach that devised from nature aspect. Such type of approaches commonly needs extra objective function evaluations that compared with gradient search techniques. These techniques offer stunning features like easiness in the numerical implementation for both discrete and continuous optimization problems and more powerful solution creations for seeking the global solutions. After the detail exploration of diversity concept focused on qualification and quantification studies, this paper presents new mutation strategy and operator to give useful diversity in the swarm.
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