Multi-Objective Evolutionary Algorithm with Random Disturbance of Different Population Mutation Strategies - Jinsuo Zhang, Qinxia Hao, Lianlian Wang (Forest Chemicals Review, 2021)
In order to improve the convergence and diversity of the non-dominated solution set of multi-objective optimization problems, and solve the problem that the algorithm is easy to fall into the local optimum in the later stage, according to the characteristics of different differential evolution strategies, an adaptive differential evolution based on the improved Chebyshev mechanism is proposed. Strategy decomposition multi-objective evolutionary algorithm (MOEA/D-ADE-levy). First, the mixed-level orthogonal experiment is used to generate uniform weight vectors and applied to improve the Chebyshev mechanism to decompose the sub-problems to obtain a uniformly distributed initial population; secondly, the population is divided into excellent individuals, intermediate individuals and poor individuals, and different individuals are used. The mutation strategy uses an adaptive mechanism for the mutation factor F and the crossover probability CR to improve the convergence and diversity of the non-dominated solution set; finally, the levy random perturbation is added to the solution set that falls into the local optimum to increase its global search ability. Jump out of the local optimum. The DTLZ test function is used to verify the effectiveness of the algorithm, and the proposed algorithm is compared with common algorithms such as NSGA2, NSGA3, MOEA, MOEA-DE, etc., and the diversity and convergence analysis of the algorithm is performed using GD and IGD evaluation indicators. The results show that the algorithm has been improved and improved in terms of convergence and diversity, and can obtain a better Pareto solution set.