Improved Simplified Particle Swarm Optimization Based on Piecewise Nonlinear Acceleration Coefficients and Mean Differential Mutation Strategy

Meijin Lin,Danfeng Chen,Zhenyu Wang,Fei Wang

doi:10.1109/access.2020.2994984

Abstract

Particle swarm optimization (PSO) has been widely used in various optimization fields because of its easy implementation and high efficiency. However, it suffers from some limitations like slow convergence and premature convergence when solving high-dimensional optimization problems. This paper attempts to address these open issues. Firstly, a new method of parameter adjustment named piecewise nonlinear acceleration coefficients is introduced to the simplified particle swarm optimization algorithm (SPSO), and an improved algorithm called piecewise-nonlinear-acceleration-coefficients-based SPSO (P-SPSO) is proposed. Then, a mean differential mutation strategy is developed for the update mechanism of P-SPSO, and another improved algorithm named mean-differential-mutation-strategy embedded P-SPSO (MP-SPSO) is proposed. To validate the performance of the proposed algorithms, four different sets of experiments are carried out in this paper. The results show that, 1) the proposed P-SPSO can get better solutions than other four classic improved SPSO with different acceleration coefficients, 2) the proposed MP-SPSO algorithm shows better optimization performance than P-SPSO and mean-differential-mutation-strategy-based SPSO (M-SPSO), 3) the proposed MP-SPSO is clearly seen to be more successful than other eight well-known PSO variants, 4) compared to other nine intelligent optimization algorithms, MP-SPSO achieves better performance in terms of solution quality and robustness. Moreover, the proposed MP-SPSO algorithm is successfully applied to a real constrained engineering problem and provides better solutions than other methods.

Highlights

In recent years, swarm intelligence optimization algorithms have become a preferred method to solve complex and non-linear problems in engineering applications, which are difficult to be solved by traditional methods
The proposed MP-simplified PSO algorithm (SPSO) algorithm is successfully applied to a real constrained engineering problem and provides better solutions than other methods
To illustrate the effect of the proposed piecewise nonlinear acceleration coefficients (PNAC) on SPSO, piecewisenonlinear-acceleration-coefficients-based SPSO (P-SPSO) is compared with basic SPSO and four typical improved SPSO with different acceleration coefficients

Summary

INTRODUCTION

Swarm intelligence optimization algorithms have become a preferred method to solve complex and non-linear problems in engineering applications, which are difficult to be solved by traditional methods. To improve the performance of PSO, a new parameter adjustment strategy named piecewise nonlinear acceleration coefficients (PNAC) is considered in this paper and a modified algorithm named piecewisenonlinear-acceleration-coefficients-based SPSO (P-SPSO) is developed. B. THE PROPOSED MEAN DIFFERENTIAL MUTATION STRATEGY (MDM) In PSO, particles update their velocities and positions according to personal historical best position (pij) and the global. It will make particles trap into the local optima and miss opportunities of jumping to far better optima To address these issues, learning from the differential evolution algorithm (DE) [44] and mean particle swarm optimization algorithm (MeanPSO) [45], an improved evolutionary mechanism named mean differential mutation strategy is developed for the P-SPSO algorithm. For the pseudo code of P-SPSO is nearly the same as MP-SPSO except the updating equations, which is not listed in detail here

EXPERIMENTS AND RESULTS ANALYSIS

A REAL ENGINEERING PROBLEM

CONCLUSION