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一种改进的鲁棒多目标优化方法 被引量：5

An improved robust multi-objective optimization method
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摘要 针对在解决某些复杂多目标优化问题过程中,所得到的Pareto最优解易受设计参数或环境参数扰动的影响.引入了鲁棒的概念并提出一种改进的鲁棒多目标优化方法,它利用了经典的基于适应度函数期望和方差方法各白的优势,有效地将两种方法结合在一起.为了实现该方法,给出一种基于粒子群优化算法的多目标优化算法.仿真实例结果表明,所给出的方法能够得到更为鲁棒的Pareto最优解. In the process of solving some complex multi-objective optimization problems, Pareto optimal solutions obtained are vulnerable to the effects of design parameters or environment parameters perturbation. Therefore, the robust solution is considered and an improved robust multi-objective optimization method is proposed. The method takes advantage of the expectation and variance of fitness fuction value, which are combined effectively. Then, a specific multi-objective evolutionary algorithm（MOEA） based on particle swarm optimization（PSO） is proposed. The simulation results show that, more robust Pareto optimal solutions can be obtained by using the improved method.
作者 徐鸣 马龙华 顾江萍 黄跃进 沈希
机构地区 浙江工业大学机械工程学院 浙江大学宁波理工学院信息工程学院
出处 《控制与决策》 EI CSCD 北大核心 2013年第8期1178-1182,1189,共6页 Control and Decision
基金 国家自然科学基金项目(51076143)
关键词 参数扰动 多目标优化 鲁棒优化 parameter perturbation multi-objective optimization robust optimization
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14

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二级参考文献11

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共引文献18

同被引文献64

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控制与决策
2013年 第8期

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