Evolutionary Computation with Distance-Based Pretreatment for Multi-modal Problems

multi-modal optimization problems (MMOPs) are pivotal in industrial production and scientific research. Unlike standard optimization problems, MMOPs aim to identify multiple global solutions, offering users a variety of optimal choices. However, traditional optimization algorithms often encounter difficulties when tackling MMOPs. To overcome this challenge, we propose a pretreatment mechanism based on individual distribution information, which is devised to enhance optimization algorithms’ performance while preserving its convergence capability. We comprehensively evaluate our method’s efficacy using 20 MMOPs from the CEC2013 benchmark suite, comparing it against the widely recognized “crowding method,” a prevalent niching strategy. Our findings unequivocally showcase the effectiveness of the proposed mechanism in expediting MMOP optimization. Furthermore, we delve into an analysis elucidating the underlying reasons behind our proposal’s effectiveness for MMOPs and discuss potential topics for future enhancements.