| 变步长自适应的改进人工鱼群算法 |
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| 引用本文： | 朱旭辉,倪志伟,程美英. 变步长自适应的改进人工鱼群算法[J]. 计算机科学, 2015, 42(2): 210-216,246 |
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| 作者姓名： | 朱旭辉 倪志伟 程美英 |
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| 作者单位： | 1. 合肥工业大学管理学院 合肥230009
2. 合肥工业大学过程优化与智能决策教育部重点实验室 合肥230009 |
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| 基金项目： | 本文受国家自然科学基金(71271071),国家“863”云制造主题项目(2011AA040501),青年科学基金项目(71301041)资助 |
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| 摘 要： | 针对人工鱼群算法在函数优化中存在陷入局部最优、后期收敛速度慢及结果精度不高等问题,通过改进鱼群算法中觅食行为及自适应调整人工鱼步长,提出了一种变步长自适应的改进人工鱼群算法。证明了该算法的全局收敛性,从而增加了其理论基础。最后,10个标准函数测试结果表明,改进后的人工鱼群算法在跳出局部最优、收敛速度、精度和稳定性方面都优于原鱼群算法和萤火虫算法,在结果精度和稳定性方面优于文献[9,23,24]的方法。
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| 关 键 词： | 人工鱼群算法 变步长 自适应步长 全局收敛 函数优化 |
| 收稿时间： | 2014-03-24 |
| 修稿时间： | 2014-07-29 |
Self-adaptive Improved Artificial Fish Swarm Algorithm with Changing Step |
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| ZHU Xu-hui,NI Zhi-wei and CHENG Mei-ying. Self-adaptive Improved Artificial Fish Swarm Algorithm with Changing Step[J]. Computer Science, 2015, 42(2): 210-216,246 |
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| Authors: | ZHU Xu-hui NI Zhi-wei CHENG Mei-ying |
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| Affiliation: | School of Management,Hefei University of Technology,Hefei 230009,ChinaKey Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education,Hefei 230009,China,School of Management,Hefei University of Technology,Hefei 230009,ChinaKey Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education,Hefei 230009,China and School of Management,Hefei University of Technology,Hefei 230009,ChinaKey Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education,Hefei 230009,China |
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| Abstract: | The artificial fish swarm algorithm in function optimization problems has some defectives,such as falling into local optimum value,converging slowly in the later period and acquiring solutions inaccurately.In order to overcome these shortcomings,a new self-adaptive artificial fish swarm algorithm with changing step was proposed by improving foraging behavior and adjusting self-adaptive step of artificial fish swarm algorithm.In addition,the paper strengthened the theoretical basis of the algorithm by proving the global convergence.Finally,the experimental results of 10 typical functions show that the proposed algorithm is superior to the original artificial fish swarm algorithm and artificial glowworm swarm optimization algorithm in overcoming the local optimum,convergence efficiency,computational precision and stability.Furthermore, the method is superior to the paper [23],[24] and [9] in computational precision and stability. |
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| Keywords: | Artificial fish swarm algorithm Changing step Self-adaptive step Global convergence Function optimization |
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