A MODIFIED PARTICLE SWARM OPTIMIZATION WITH RANDOM ACTIVATION FOR INCREASING EXPLORATION

Alrijadjis Alrijadjis

doi:10.28961/kursor.v8i1.72

Abstract

Particle Swarm Optimization (PSO) is a popular optimization technique which is inspired by the social behavior of birds flocking or fishes schooling for finding food. It is a new metaheuristic search algorithm developed by Eberhart and Kennedy in 1995. However, the standard PSO has a shortcoming, i.e., premature convergence and easy to get stack or fall into local optimum. Inertia weight is an important parameter in PSO, which significantly affect the performance of PSO. There are many variations of inertia weight strategies have been proposed in order to overcome the shortcoming. In this paper, a new modified PSO with random activation to increase exploration ability, help trapped particles for jumping-out from local optimum and avoid premature convergence is proposed. In the proposed method, an inertia weight is decreased linearly until half of iteration, and then a random number for an inertia weight is applied until the end of iteration. To emphasis the role of this new inertia weight adjustment, the modified PSO paradigm is named Modified PSO with random activation (MPSO-RA). The experiments with three famous benchmark functions show that the accuracy and success rate of the proposed MPSO-RA increase of 43.23% and 32.95% compared with the standard PSO.

Highlights

It is too difficult to solve optimization problems in large-scale complex engineering using conventional optimization technique
Particle Swarm Optimization (PSO) using linearly decreasing inertia weight (PSO-LDW) is most commonly used or standard type of PSO and it can improve the performance of PSO to some extent, but it may be trapped in local optimum and fail to achieve high search accuracy
This section compares the performances of the proposed Modified PSO with random activation (MPSO-RA) with the standard type of PSO or PSO-LDW

Summary

Introduction

It is too difficult to solve optimization problems in large-scale complex engineering using conventional optimization technique. PSO using linearly decreasing inertia weight (PSO-LDW) is most commonly used or standard type of PSO and it can improve the performance of PSO to some extent, but it may be trapped in local optimum and fail to achieve high search accuracy. A random activation is applied to generate a new inertia weight randomly in order to help particles jump out from local optimum.

Results

Conclusion