A modified PSO algorithm with dynamic parameters for solving complex engineering design problem

ABSTRACT

This paper proposed a new approach of particle swarm optimization (PSO). The proposed modified PSO algorithm is equipped with some specially designed mechanisms of adaptively updating algorithm parameters to preserve the diversity of the swarm and to keep the balance between exploration and exploitation searches. All these mechanisms help the algorithm to avoid the premature convergence and to strengthen its robustness. Experiments are conducted on different complicated, unimodal and multimodal test functions, as well as a typical engineering inverse problem, the TEAM Workshop problem 22. The numerical results illustrate that the proposed PSO shows better performance as compared to other well developed evolutionary algorithms.

KEYWORDS:

• Dynamic learning parameters
• dynamic inertia weight
• particle swarm optimization
• global optimization
• inverse problem

2010 AMS SUBJECT CLASSIFICATIONS:

• 20B40
• 90C26
• 97R40
• 15A29
• 78A25

Disclosure statement

No potential conflict of interest was reported by the authors.