Enhanced opposition differential evolution algorithm for multimodal optimization

Abstract

Most of the real-world problems are multimodal in nature that consists of multiple optimum values. Multimodal optimization amounts to finding multiple global and local optima (as opposed to a single solution) of a function so that the user can have a better knowledge about di↵erent optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable one while still maintaining the optimal system performance. Classical gradient-based methods fail for optimization problems in which the objective functions are either discontinuous or non-di↵erentiable. Evolutionary Algorithms (EAs), due to their population-based approaches, can detect multiple solutions within a population in a single simulation run and have a clear advantage over the classical optimization techniques, which need multiple restarts and multiple runs in the hope that a di↵erent solution may be dis covered every run, with no guarantee. Hence, several EAs have been proposed to solve such kinds of problems. However, Di↵erential Evolution (DE) algorithm is a population-based heuristic method that can solve such optimization problems, and it is simple to implement.