A Chaotic Hybrid Cultural Algorithm for Constrained Optimization

This paper proposes a new chaotic hybrid cultural algorithm to solve constrained optimization problems. In the proposed method, differential evolution is embedded into cultural algorithm as its population space and applies situational and normative knowledge sources in belief space to influence the variation operator of differential evolution. We apply chaos theory to obtain self-adaptive parameter settings for differential evolution and then simple feasibility-based selection comparison techniques are devised to handle constraints effectively. The performance of the proposed method has been examined by its application to eleven test cases. The results are competitive with respect to three state-of-the-art evolutionary optimization algorithms.