A Comparison of Crowding Differential Evolution Algorithms for Multimodal Optimization Problems

Multimodal problems are related to locating multiple, redundant global optima, as opposed to single solution. In practice, generally in engineering problems it is desired to obtain many redundant solutions instead of single global optima since the available resources cannot be enough or not possible to implement the solution in real-life. Hence, as a toolbox for finding multimodal solutions, modified single objective algorithms can able to use. As one of the fundamental modification, from one of the niching schemes, crowding method was applied to Differential Evolution (DE) algorithm to solve multimodal problems and frequently preferred to compared with developed methods. Therefore, in this study, eight different DE are considered/evaluated on ten benchmark problems to provide best possible DE algorithm for crowding operation. In conclusion, the results show that the time varying scale mutation DE algorithm outperforms against other DE algorithms on benchmark problems.