Differential evolution mutation operators for constrained multi-objective optimization

Many real-world optimization problems belong to constrained multi-objective optimization problems (CMOPs). Handling constraints and optimizing objectives are two equally important goals. With effective and efficient population-based meta-heuristics in mind, how to generate the offspring with good convergence and diversity properties is a problem to be solved. Competitive algorithms based on different evolution (DE) metaphors have been proposed to solve CMOPs over years as the performance of the DE is attractive. The creative idea of the proposed algorithm is to design a novel mutation mechanism for handling infeasible solutions and feasible solutions respectively. The mechanism can produce well distributed Pareto optimal front while satisfying all concerning constraints. The performance of the algorithm is evaluated on nineteen benchmark functions. Compared with three representative constraint handling techniques and latest optimization algorithms, experimental results have indicated that the proposed algorithm is an effective candidate for real-world problems. At last, the proposed method is used to solve combined economic emission dispatch (CEED) problem. The experiment results have further validated the efficiency of the method.