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| 求解约束优化问题的多目标粒子群算法* | |
| 引用本文： | 刘衍民,牛奔,赵庆祯. 求解约束优化问题的多目标粒子群算法*[J]. 计算机应用研究, 2011, 28(3): 851-853. DOI: 10.3969/j.issn.1001-3695.2011.03.014 |
| 作者姓名： | 刘衍民 牛奔 赵庆祯 |
| 作者单位： | 1. 遵义师范学院,数学系,贵州,遵义,563002;山东师范大学,管理与经济学院,济南,250014 2. 深圳大学,管理学院,广东,深圳,518060 3. 山东师范大学,管理与经济学院,济南,250014 |
| 基金项目： | 国家高科技发展规划项目（“863”计划） |
| 摘 要： | 提出一种多目标粒子群算法处理约束优化问题(MOCPSO). 首先将约束优化问题转化为多目标问题, 然后给出一个不可行阈值来充分地利用不可行粒子的信息引导种群的飞行; 并提出一种粒子间的比较准则以比较它们的优劣; 最后, 为了增加种群的多样性, 提升种群跳出局部最优解的能力, 引入高斯白噪声扰动. 选取有代表性的标准测试函数对MOCPSO算法的性能进行仿真实验, 相比较其它算法, 结果显示MOCPSO算法是求解约束优化问题的有效算法. |
| 关 键 词： | 多目标 约束 粒子群算法 |
| 收稿时间： | 2010-08-20 |
| 修稿时间： | 2011-01-27 |
Multi-objective particle swarm optimizer for solving constraint optimization problems |
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| LIU Yan-min,NIU Ben,ZHAO Qing-zhen. Multi-objective particle swarm optimizer for solving constraint optimization problems[J]. Application Research of Computers, 2011, 28(3): 851-853. DOI: 10.3969/j.issn.1001-3695.2011.03.014 | |
| Authors: | LIU Yan-min NIU Ben ZHAO Qing-zhen |
| Affiliation: | LIU Yan-min1,2,NIU Ben3,ZHAO Qing-zhen2(1.Dept.of Mathematics,Zunyi Normal College,Zunyi Guizhou 563002,China,2.School of Management & Economics,Shandong Normal University,Jinan 250014,3.College of Management,Shenzhen University,Shenzhen Guangdong 518060,China) |
| Abstract: | An improved multi-objective particle swarm optimizer is proposed for solving constrained optimization problem (MOCPSO for short). In MOCPSO, firstly, the constraint optimization problem was converted into multi-objective problems, and then the infeasible threshold value was introduced to make the best of infeasible solution message to lead the swarm flight. A new comparison strategy was proposed to compare any two particles. Finally, in order to increase the diversity of the swarm and improve the ability to escape from the local optima, the Gaussian white noise disturbance was proposed. The results of experiment on benchmark test functions verify the effectiveness of the proposed method. The proposed MOCPSO algorithm is effective compared with other state-of-the-art approaches |
| Keywords: | Multi-objective Constraint Particle swarm optimizer |
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