Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems

Abstract

This paper presents an improved particle swarm optimizer (PSO) for solving multimodal optimization problems with problem-specific constraints and mixed variables. The standard PSO is extended by employing a comprehensive learning strategy, different particle updating approaches, and a feasibility-based rule method. The experiment results show the algorithm located the global optima in all tested problems, and even found a better solution than those previously reported in the literature. In some cases, it outperforms other methods in terms of both solution accuracy and computational cost.

Keywords:

• Particle swarm optimization
• mixed variables
• feasibility-based rules
• constrained optimization
• evolutionary algorithms
• comprehensive learning strategy