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Cooperative Multi-objective Optimization Algorithm Based on Reward and Punishment Mechanism

WU Daqing  1,2,3,SHAO Ming  4,LI Quan  1,LI Kang  2   

  1. (1.School of Computer Science and Technology,University of South China,Hengyang 421001,China; 2.Glorious Sun School of Business and Management,Donghua University,Shanghai 200051,China; 3.Key Laboratory of Intelligent Computing & Signal Processing,Ministry of Education,Hefei 230039,China; 4.School of Management,Shanghai University of Engineering Science,Shanghai 200051,China)
  • Received:2014-08-11 Online:2015-10-15 Published:2015-10-15

基于奖惩机制的协同多目标优化算法

伍大清1,2,3,邵明4,李悛1,李康2   

  1. (1.南华大学计算机科学与技术学院,湖南 衡阳421001; 2.东华大学旭日工商管理学院,上海200051; 3.教育部计算智能与信号处理重点实验室,合肥 230039;4.上海工程技术大学管理学院,上海 200051)
  • 作者简介:伍大清(1982-),女,讲师、博士,主研方向:多目标智能决策,智能计算;邵明,副教授、博士;李悛、李康,讲师、博士。
  • 基金资助:
    湖南省教育厅基金资助项目“基于协同演化计算的不确定信息车辆路径问题研究”(13C818);湖南省衡阳市科技局科技计划基金资助项目“自学习演化计算在智能交通控制中的应用研究”(2013KG63);教育部人工智能重点实验室基金资助项目“基于冷链云配送模式的车辆路径优化模型及协同控制研究”。

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

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