Improved Embedded Micro-PSO for High-Dimensional Optimization Problems

Abstract:

This paper presents a novel optimization technique called embedded micro-particle swarm optimization (EMPSO) to solve high-dimensional problems with continuous variables. The proposed EMPSO adopts a population memory which is divided into two portions as the source of diversity, and an external memory to collect particles performing well in an embedded PSO with a very small population size. However, the fact that the new method doesn’t excel in all of the benchmark functions highlights the necessity of developing improvement. Thus an adaptive mutation operator is introduced into EMPSO to remedy the issue. The experimental results show that the improved EMPSO has good performance for solving large-scale optimization problems.

• Periodical: Applied Mechanics and Materials (Volumes 236-237)
• Pages: 1184-1189
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.236-237.1184
• Online since: November 2012
• Authors: Wen Hua Han, Chang Dong Zhu
• Keywords: Adaptive Mutation Operator, Embedded Micro-PSO, High-Dimensional

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