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论文:2012,Vol:30,Issue(2):286-290 |
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引用本文： |
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鲁鹏, 章卫国, 李广文, 刘小雄, 李想. 一种基于杂草克隆的多目标粒子群算法[J]. 西北工业大学 |
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Lu Peng, Zhang Weiguo, Li Guangwen, Liu Xiaoxiong, Li Xiang. A New and Efficient Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm Based on Invasive Weed Cloning[J]. Northwestern polytechnical university |
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| 一种基于杂草克隆的多目标粒子群算法 |
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| 鲁鹏, 章卫国, 李广文, 刘小雄, 李想 |
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| 西北工业大学 自动化学院,陕西 西安 710072 |
| 摘要: |
| 多目标粒子群算法(MOPSO)在优化函数时,尤其对于Pareto前沿是分段不连续的优化函数,存在收敛速度慢,种群多样性差的缺陷。针对此问题,将杂草克隆机制引入MOPSO,提出了一种新的多目标粒子群算法,称之为IWMOPSO。该算法利用改进的档案维护策略和不可行解增强多样性和均匀性,通过标准测试函数验证,能够使所求得的Pareto最优解逼近整个Pareto真实前沿,收敛性和多样性明显优于MOPSO和NSGA-Ⅱ,具有较强的应用性。 |
| 关键词:
多目标算法
粒子群算法
Pareto前沿
杂草克隆
MOPSO
NSGA-Ⅱ
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| A New and Efficient Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm Based on Invasive Weed Cloning |
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| Lu Peng, Zhang Weiguo, Li Guangwen, Liu Xiaoxiong, Li Xiang |
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| Department of Automatic Control,Northwestern Polytechnical University,Xi'an 710072,China |
| Abstract: |
| When the existing MOPSO algorithm is applied to optimizing the functions with the discontinuous Paretofront, its convergence and the diversity of its population are poor. To solve the problem, we propose our new IW-MOPSO (Invasive Weed MOPSO) algorithm, which we believe is more efficient than existing ones. Sections 1through 2 of the full paper explain our new IWMOPSO algorithm. Section 1 presents the defects of the MOPSO algo-rithm. Section 2 explains how to reduce such defects to a minimum. Section 3 uses five benchmark test functions tocompare the performance of our new IWMOPSO algorithm with those of the existing MOPSO and NSGA-Ⅱ algo-rithms. The test results, given in Tables 1 and 2 and Fig. 7, and their analysis show preliminarily that both the con-vergence of our IWMOPSO algorithm and its diversity are enhanced by the improved file maintenance strategy andthe unfeasible solutions, with the Pareto front obtained with our new algorithm very close to the real Pareto front, thus being more efficient than both the MOPSO and NSGA-Ⅱ algorithms. |
| Key words:
convergence of numerical methods
defects
efficiency
evolutionary algorithms
functions
mainte-nance
mechanisms
multiobjective optimization
particle swarm optimization;analysis
Pareto front
invasive weed cloning
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| 收稿日期: 2011-06-21
修回日期:
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| DOI: |
| 基金项目: 航空科学基金(20090753008)资助 |
| 通讯作者:
Email： |
| 作者简介: 鲁鹏(1987-),西北工业大学硕士研究生,主要从事飞行器控制、智能进化算法研究。
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| 相关功能 |
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| 作者相关文章 |
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| 鲁鹏 在本刊中的所有文章 |
| 章卫国 在本刊中的所有文章 |
| 李广文 在本刊中的所有文章 |
| 刘小雄 在本刊中的所有文章 |
| 李想 在本刊中的所有文章 |
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参考文献: |
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[1] Coello C A, et al. Handling Multiple Objectives with Particle Swarm Optimization. IEEE Trans on Evolutionary Computation, 2004, 8(3): 256-279
[2] Chamaani S, Mirtaheri S A. Improvement of Time and Frequency of Antipodal Vivaldi Antenna Using Multi-Objective ParticleSwarm Optimization. IEEE Trans on Antennas and Propagation, 2011(59):1738-1742
[3] Mehrabian A R, Lucas C. A Novel Numerical Optimization Algorithm Inspired from Weed Colonization. Ecological Informatics, 2006, 1(4): 355-366
[4] 雷德明, 严新平. 多目标智能优化算法及应用. 北京:科学出版社, 2009Lei Deming, Yan Xinping. Multi-Objective Evolutionary Optimization and Application. Beijing Science Press, 2009 (in Chinese) |
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