| 一种改进的多目标粒子群优化算法及其应用 |
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| 引用本文： | 冯金芝,陈 兴,郑松林.一种改进的多目标粒子群优化算法及其应用[J].计算机应用研究,2014,31(3):675-678. |
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| 作者姓名： | 冯金芝 陈 兴 郑松林 |
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| 作者单位： | 上海理工大学 a. 机械工程学院; b. 机械工业汽车底盘机械零部件强度与可靠性评价重点实验室, 上海 200093 |
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| 基金项目： | 国家“十二五”“863”计划重大项目（2011AA11A265, 2012AA110701）; 国家自然科学基金资助项目（50875173）; 上海市科委科研计划资助项目（11140502000）; 上海汽车工业科技发展基金资助项目（1104） |
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| 摘 要： | 为提高多目标粒子群优化 (MOPSO)算法处理多目标优化问题的性能, 降低计算复杂度, 改善算法的收敛性, 提出了一种改进的多目标粒子群优化算法。通过运用比例分布及跳数改进机制策略的方法, 使该算法不仅继承了MOPSO算法的优点, 而且具有很强的局部搜索能力和较好的鲁棒性能, 使非劣解集均匀分布, 尽可能逼近真实的非劣前沿。通过对多连杆悬架空间结构硬点的多目标优化, 进一步验证了该算法的实用性及其优越性。
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| 关 键 词： | 多目标粒子群优化 比例分布 跳数改进机制 多连杆悬架 |
Improved MOPSO algorithm and its application |
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| Affiliation: | a. School of Mechanical Engineering, b. Machinery Industry Key Laboratory for Mechanical Strength & Reliability Evaluation of Auto Chassis Components, University of Shanghai for Science & Technology, Shanghai 200093, China |
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| Abstract: | In order to enhance the multi-objective particle swarm optimization (MOPSO) algorithm processing performance for multi-objective optimization, reduce the computational complexity and improve the convergence of algorithm, this paper put forward an improved multi-objective particle swarm optimization algorithm, which used proportional distribution and jump improved mechanism, not only inherited the advantages of MOPSO algorithm, but had a strong local searching ability, good robust performance and uniform non-inferior solution set, as far as possible approximation real non-inferior front. The practicability and superiority of the proposed algorithm is verified by applying it into multi-objective optimization of the spatial structure geometry parameters of a multi-link suspension. |
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| Keywords: | Keywords: multi-objective particle swarm optimization proportional distribution jump improved mechanism multi-link suspension |
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