Multi-objective chaotic differential evolution algorithm with grading second mutation

To solve the multi-objective constraint optimization problem, this paper proposes an advanced differential evolution(DE). In the proposed algorithm, grading second mutation and chaotic theory are combined into standard DE. At early evolution process of DE, random second mutation based on non-dominance Pareto solution is adopted in order to improve global exploring ability. And in the later evolution process, the chaotic second mutation based on non-dominance Pareto solution is added into DE evolution operation in order to enhance local searching ability of algorithm. By testing benchmarks functions, simulation results show that, this algorithm has better convergence and distribution property, and is superior to standard DE in keeping balance between diversity and convergence.