A PSO-based algorithm with local search for multimodal optimization without constraints

The purpose of this paper is to present a PSO algorithm mixed with a new hybrid local search algorithm named LHS, enhancing the exploration and exploitation capabilities of the canonical PSO. The hybrid PSO, named PSOLHS, is examined against six known multimodal functions and compared with both canonical PSO and LHS. Furthermore, a comparison between evolutionary strategies (ES) and MPSO-LS is going to show how our approach outperforms these other techniques in almost all benchmark functions. All comparisons are based on a statistical t-test for supporting our results.