Particle swarm optimization with complementary velocity learning scheme and multiple performance mechanisms: applications in power electronics

The integration of multiple operators and mechanisms in metaheuristics is an effective methodology for improving their performance. Meanwhile, to further enhance the level of intelligence in metaheuristics, both the complexity of the target problem and learning strategies should be considered when designing metaheuristic algorithms. In this work, a Complementary velocity learning scheme, an Improved estimation of distribution algorithm, Information archiving, and the Interior-point method based Particle Swarm Optimization (CIII-PSO) is proposed. The complementary velocity learning scheme not only combines three different velocity update strategies with distinct exploration and exploitation capabilities, but also integrates learning strategies to better guide particles during swarm-based search. The improved estimation of distribution algorithm accounts for both the complexity of the target problem and the enhancement of exploitation. The information archiving mechanism generates both parameters and archived particles, providing rich options and historical information (parameters and particles from early iterations) for learning. The interior-point method is a local search mechanism that explores the neighborhood of the best particles, thereby greatly enhancing the exploitation capability of CIII-PSO. CIII-PSO is evaluated against 15 recently proposed PSO-based algorithms on the IEEE Congress on Evolutionary Computation 2013 and 2017 (CEC2013 and CEC2017) benchmark suites, demonstrating superior performance across all test functions. Ablation studies further verify the effectiveness and individual contributions of the proposed components. In addition, CIII-PSO is applied to power electronics problems, achieving outstanding performance in the experiments.