Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems
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.
particle swarm optimization mixed variables comprehensive learning strategy feasibility-based rules constrained optimization evolutionary algorithms