| 基于两阶段领导的多目标粒子群优化算法 |
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| 引用本文： | 胡广浩,毛志忠,何大阔.基于两阶段领导的多目标粒子群优化算法[J].控制与决策,2010,25(3):404-410. |
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| 作者姓名： | 胡广浩 毛志忠 何大阔 |
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| 作者单位： | 1. 东北大学信息科学与工程学院,沈阳,110004
2. 东北大学信息科学与工程学院,沈阳,110004;东北大学流程工业综合自动化教育部重点实验室,沈阳,110004 |
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| 基金项目： | 国家863计划项目(2006AA060201) |
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| 摘 要： | 提出一种基于两阶段领导的多目标粒子群算法(P-AMOPSO).该算法包含4个改进策略:基于强支配排序与拥挤距离排序相结合的构造外部集策略,基于两阶段的领导粒子选择策略,基于高斯分布及均匀分布相结合的变异策略,基于邻域认知的个体极值更新策略.通过几个典型的多目标测试函数对P-AMOPSO算法的性能进行测试,并与多目标优化算法进行对比.结果表明,P-AMOPSO算法具有较好的搜索性能.
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| 关 键 词： | 粒子群算法 多目标优化 两阶段领导 变异 |
| 收稿时间： | 2009/5/15 0:00:00 |
| 修稿时间： | 2009/8/26 0:00:00 |
Multi-objective PSO algorithm based on combining Two Stages-guided and cross-mutation |
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| Hu Guang-hao,MAO Zhi-zhong,HE Da-kuo.Multi-objective PSO algorithm based on combining Two Stages-guided and cross-mutation[J].Control and Decision,2010,25(3):404-410. |
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| Authors: | Hu Guang-hao MAO Zhi-zhong HE Da-kuo |
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| Affiliation: | a.College of Information Science and Engineering/a>;b.Key Laboratory of Integrated Automation of Process Industry/a>;Ministry of Education/a>;Northeastern University/a>;Shenyang 110004/a>;China. |
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| Abstract: | This paper introduces a multi-objective particle swarm optimization algorithm (P-AMOPSO) based on two stages-guided,which includes four improved strategies,the strategy of constructing external data set based on combining strong predominance ranking and crowding distance ranking,the strategy of selecting guided particle based on two stages,the strategy of mutation based on combining Gaussian distribution mutation and uniform distribution mutation,and the strategy of updating based on personal best of neighb... |
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| Keywords: | PSO Multi-objective optimization Two stages-guided Mutation |
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