Differential Evolution Algorithm for Multimodal Optimization: A Short Survey

Most of the real-world problems are multimodal in nature. Several algorithms have been proposed to solve multimodal optimization problems. Classical gradient-based methods fail for optimization problems in which functions are either discontinuous or non-differentiable. Differential Evolution (DE) is simple to implement population-based heuristic method used for solving optimization problems even if the function is discontinuous or non-differentiable. It is proved to have one of the fastest rates of convergence toward the optima. The search behavior of DE algorithm is governed by its parameters. DE has won top ranks in many IEEE CEC competitions as it has outperformed its competitors in solving real parameter space optimization problems. DE and its variants have also been applied to solve various engineering optimization problems. This paper aims to cover the work done in the area of real parameter single objective multimodal optimization using differential evolution algorithm.