Performance comparison of differential evolution and soma on chaos control optimization problems

Abstract

This paper compares the performance of Differential Evolution (DE) with Self-Organizing Migrating Algorithm (SOMA) in the task of optimization of the control of chaos. The main aim of this paper is to show that evolutionary algorithms like DE are capable of optimizing chaos control, leading to satisfactory results, and to show that extreme sensitivity of the chaotic environment influences the quality of results on the selected EA, construction of cost function (CF) and any small change in the CF design. As a model of deterministic chaotic system, the two-dimensional Henon map is used and two complex targeting cost functions are tested. The evolutionary algorithms, DE and SOMA were applied with different strategies. For each strategy, repeated simulations demonstrate the robustness of the used method and constructed CF. Finally, the obtained results are compared with previous research.