Abstract: To improve the convergence and distribution of Multi-objective Evolutionary Algorithm(MOEA) in dealing with large-dimensional Multi-objective Optimization Problem(MOP),a multi-objective particle swarm optimization algorithm based on human disciplinary behavior is proposed.The strategies such as promoting/punishment factor,the elite learning strategy as well as restructuring topology structure strategy with dynamic population in period are introduced in proposed algorithm,to make the algorithm have strong global search ability and good robust performance.Some typical multi-objective optimization functions are tested to verify the algorithm,and simulation results show that,compared with recent other algorithms,the algorithm can ensure good convergence while having uniform distribution and wild coverage area.

Key words: multi-objective optimization algorithm, cooperative, elite learning strategy, topology structure, reward and punishment mechanism

摘要： 为提高已有多目标优化算法在求解高维复杂多目标优化问题上的解集分布性和收敛性,提出一种新的多目标微粒群优化算法。该算法基于多目标协同框架,将多种群奖惩机制进化算法用于求解分解后的若干单目标优化子问题,采用动态环形的拓扑结构,设计一种新型精英学习策略,获得逼近Pareto前沿的最优解集。通过典型的多目标优化函数进行测试验证,结果表明,与现有多目标优化算法相比,该算法不仅具有较好的收敛性能,而且解集分布性更均匀、覆盖范围更广。

关键词: 多目标优化算法, 协同, 精英学习策略, 拓扑结构, 奖惩机制

CLC Number: 

• TP301