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一种基于多样性度量的多目标化进化算法 被引量：4

New multi-objectivization evolutionary algorithm based on individual diversity
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摘要 针对如何通过附加的方法对多目标化问题进行理论分析,提出并证明了选择附加函数的3个前提条件.提出一种多目标化进化算法,根据种群中个体的多样性度量进行多目标化,并采用改进的非劣分类遗传算法对构造所得的多目标优化问题进行多目标优化.在静态和动态两种环境下进行算法性能验证,结果表明,在种群多样性保持、处理欺骗问题、动态环境下的适应能力等方面,所提算法明显优于其他同类算法. This paper investigates the use of multi-objective evolutionary algorithms to solve single-objective optimization problems, and an instructional theory is proposed for constructing reasonable additional objective. A multi-objective optimization algorithm based on multi-objectivization is also proposed. Single-objective problems are changed to multi-objective problems based on individual diversity. The nondominated sorting genetic algorithm is used as multi-objective optimization algorithm. The computation results show that the proposed algorithm has the approving performance in maintaining population diversity, dealing schema deception problems and dynamic optimization.
作者 陈昊 黎明 张可
机构地区 南京航空航天大学自动化学院 南昌航空大学无损检测技术教育部重点实验室 南京航空航天大学经济与管理学院
出处 《控制与决策》 EI CSCD 北大核心 2010年第9期1343-1348,共6页 Control and Decision
基金 国家自然科学基金项目(60963002) 航空科学基金项目(2008ZD56003) 江西省教育厅基金项目(GJJ08209)
关键词 多目标化 附加函数 动态环境 进化算法 移动峰问题 Multi-objectivization Additional objective Dynamic environment Multi-objective optimization Moving peaks benchmark
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献12

  • 1Jahne M, Li X D, Branke J. Evolutionary algorithms and multi-objectivization for the travelling salesman problem[C]. Proc of CECCO'2009. Montreal, 2009: 595- 602.
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二级参考文献12

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共引文献16

同被引文献44

引证文献4

二级引证文献12

控制与决策
2010年 第9期

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