Adaptive memetic differential evolution with niching competition and supporting archive strategies for multimodal optimization

Multimodal optimization, which aims at locating multiple optimal solutions within the search space, is inherently a difficult problem. This work proposes an adaptive memetic differential evolution algorithm with niching competition and supporting archive strategies to tackle In proposed algorithm, strategy designed competitively employ niches according their potentials by encouraging high potential for exploitation while low exploration, thus appropriately searching space identify optima . Further, devised implemented niche level dual purpose of helping maintain as well facilitate population. this strategy, writing reading implicitly during rather than requiring external rules. Additionally, Cauchy-based local scheme, considers possible locations implement search, developed incorporated into method efficiently properly improve seeds. The resulting has been evaluated extensive experiments on benchmark functions robot kinematics problem compared related methods. results show that our able consistently accurately locate in solution outperform