Title: "Double Inertia Weight-Based Particle Swarm Optimization"
         

DOI: 10.15224/978-1-63248-084-2-33
Page(s): 18 - 24
Authors: CHE-NAN KUO , CHING-MING LAI, YU-HUEI CHENG

Abstract

Particle swarm optimization (PSO) is a well-known and popular swarm intelligence algorithm. The inertia weight of a PSO plays the crucial role in the ability of exploration and exploitation. Many strategies for adapting the inertia weight of PSO have been proposed. In this study, we use two inertia weights to improve the global and local search of PSO. Nine benchmark functions with 10 dimensions for unimodal functions, multimodal functions with many local optima, and multimodal functions with a few local optima is used as the test functions. We compare two inertia weight PSOs with the proposed method. The results show the proposed method is useful for improve the search ability of PSO.