Hybrid harmony search particle swarm optimization with global dimension selection

This study presents a hybrid harmony search particle swarm optimization with global dimension selection (HHSPSO-GDS) for improving the performance of particle swarm optimization (PSO). In HHSPSO-GDS, a new global velocity updating strategy is introduced to enhance the neighborhood region search of the current best solution and to get a better trade-off between convergence rate and robustness. Additionally, a dynamic non-linear decreased inertia weight is utilized to balance the global exploration and local exploitation. Moreover, the best-worst improvisation mechanism of harmony search (HS) is implanted in the HHSPSO-GDS algorithm and a global dimension selection is employed in the improvisation process, which can effectively accelerate convergence. Global best information sharing strategy is developed to link the two layer exploration frames (PSO and HS). Finally, a comprehensive experimental study is conducted on a large number of benchmark functions. The experimental results reveal that HHSPSO-GDS performs better in terms of the quality of solution, convergence rate, robustness and scalability compared to various state-of-the-art PSOs and other meta-heuristic search algorithms.