Multi-objective Evolutionary Algorithm Based on Dynamical Crossover and Mutation Probability

In complicated multi-objective optimization, it often happens that points in part region of Pareto front are easy to get, but in others are difficult. To obtain evenly distributed Pareto optimal solution, we construct dynamical crossover and mutation probability which can self-adaptively adjust the number of individuals engaged in crossover and mutation, combine with the fitness function constructed by weighted min-max strategy in which the weight is uniformly designed, to present a new multi-objective evolutionary algorithm (DMOEA). To evaluate the performance of our algorithm, we compare the numerical results of our algorithm with the MOEA/D-DE and NSGA-II-DE, the comparison shows that our algorithm is very efficient.