Abstract
Particle Swarm Optimization (PSO) is a member of the swarm intelligence-based on a metaheuristic approach which is inspired by the natural deeds of bird flocking and fish schooling. In comparison to other traditional methods, the model of PSO is widely recognized as a simple algorithm and easy to implement. However, the traditional PSO’s have two primary issues: premature convergence and loss of diversity. These problems arise at the latter stages of the evolution process when dealing with high-dimensional, complex and electromagnetic inverse problems. To address these types of issues in the PSO approach, we proposed an Improved PSO (IPSO) which employs a dynamic control parameter as well as an adaptive mutation mechanism. The main proposal of the novel adaptive mutation operator is to prevent the diversity loss of the optimization process while the dynamic factor comprises the balance between exploration and exploitation in the search domain. The experimental outcomes achieved by solving complicated and extremely high-dimensional optimization problems were also validated on superconducting magnetic energy storage devices (SMES). According to numerical and experimental analysis, the IPSO delivers a better optimal solution than the other solutions described, particularly in the early computational evaluation of the generation.
Highlights
Inverse problems, or real world design problems, have been recognized as an active research topic in the fields of academia and engineering sciences, and the optimal solution to such kinds of problems is difficult and hard due to the presence of multimodal cost functions
As the values of inertia weight have an imperative role in a dynamic environment, to solve real world problems in a dynamic environment, we developed a novel strategy for the inertia weight which will try to maintain the best balance between exploration and exploitation search of the candidates in the Particle Swarm Optimization (PSO) process
The results demonstrate that the novel Improved PSO (IPSO) recorded output is superior to those of the others
Summary
Inverse problems, or real world design problems, have been recognized as an active research topic in the fields of academia and engineering sciences, and the optimal solution to such kinds of problems is difficult and hard due to the presence of multimodal cost functions. Researchers have tried to design various nature-inspired algorithmic models in the state of the art to enhance the computational capabilities as well as increase the diversity of search space in engineering optimization problems. We knew that the optimization problems have more minima and one optimum solution, while the current existence of the stochastic algorithm will try to reach the global optimum region or space.
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