关键词: 多目标优化, 种群扩增, 种群缩减, 差分进化算法, 精英学习策略

Abstract: In order to improve the convergence and distribution in solving the large-dimensional multi-objective optimization problem(MOP)with differential evolution algorithm(DE), a multi-objective differential evolution algorithm based on the population self-adaptive adjustment(PSAMODE)is proposed. A population expansion strategy is designed for DE, which generates some new individuals to search optimal non dominated solutions in decision space; a population shrinking strategy is also designed, which depends on the degree of contribution of the non dominated solutions to elimination of poor individuals to reduce the computational load. Meanwhile, it sets aside some space to the new disturbance individuals with population diversity. The proposed method is introduced to elite learning strategies to prevent trapping into local convergence. Some typical multi-objective optimization functions are tested to verify this method. Simulation results show that compared with other algorithms, PSAMODE has obvious advantages, superior performance and ensure a good convergence. To obtain the Pareto optimal solution set, the proposed method has a more uniform distribution and wider coverage, especially suitable for high-dimensional complex solution of the multi-objective optimization problem.

Key words: multi-objective optimization, population expansion strategy, population shrinking strategy, differential evolution algorithm, elite learning strategy