Analysis of new differential evolution variants to solve multi-modal problems - Ramesh Khaparde, Amit; Poondi Sundarasamy, Ramesh; Rajendran, Sathiyaraj; Ticku, Amrita; Palanichamy, Arunkumar (IAES International Journal of Artificial Intelligence (IJ-AI), 2023)
Differential evolution algorithm (DE) requires diversified population to solve multi- modalproblems. DE supports non-distributed population. DE versions include differential evolution algorithm with best selection method and species evolution (DESBS) and differential evolution algorithm with hierarchical fair competition modal (HFCDE). This article analyzed the efficiency of HFCDE and DESBS to solve the multi-modal s’ problems. HFCDE and DESBS support non-distributed population structured. HFCDE starts with set of feasible solution then it distributed them in the different hierarchy. HFCDE provides the fair competition. DESBS is another semi distributed, differential evolution algorithm.It starts with set of feasible solutions (population). Later it identifies the niches and create the sub-groups within population. Both HFCDE and DESBS have outperformed the other variant of state-of-art variants of DE. Here, the performance of DESBS and the HFCDE are rigorously tested on the multimodal problems. The success of DESBS over HFCDE in multi-modal difficulties managed to overcome the phenomena of elitism to resolve the complex problems, it has been observed that DESBS performs better than HFCDE in complex multi-modal scenarios.