| 保持粒子活性的改进粒子群优化算法 |
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| 引用本文： | 陆克中,王汝传,帅小应.保持粒子活性的改进粒子群优化算法[J].计算机工程与应用,2007,43(11):35-38. |
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| 作者姓名： | 陆克中 王汝传 帅小应 |
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| 作者单位： | 安徽省池州市池州师范专科学校计算机系 南京邮电学院计算机系 |
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| 基金项目： | 国家自然科学基金
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安徽省高校青年教师科研项目 |
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| 摘 要： | 针对基本粒子群优化算法（particle swarm optimization, 简称PSO）存在的早熟收敛问题，提出了一种保持粒子活性的改进粒子群优化（IPSO）算法。当粒子失活时，对粒子进行变异或扰动操作，重新激活粒子，使粒子能够有效地进行全局和局部搜索。通过对4种Benchmark函数的测试，结果表明IPSO算法不仅具有较快的收敛速度，而且能够更有效地进行全局搜索。
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| 关 键 词： | 粒子群优化 改进的粒子群优化 进化计算 |
| 文章编号： | 1002-8331（2007）11-0035-04 |
| 收稿时间： | 2006-5-11 |
| 修稿时间： | 2006-09 |
Improving Particle Swarm Optimization by Keeping Particles Active |
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| KeZhong Lu.Improving Particle Swarm Optimization by Keeping Particles Active[J].Computer Engineering and Applications,2007,43(11):35-38. |
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| Authors: | KeZhong Lu |
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| Affiliation: | 1.Department of Computer, Chizhou Teachers College, Chizhou, Anhui 247000, China;2.College of Computer,Nanjing University of Posts and Teleeommunieations,Nanjing 210003,China |
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| Abstract: | To overcome the problem of premature convergence on Particle Swarm Optimization(PSO),this paper proposes an Improved Particle Swarm Optimization(IPSO) called keeping particles active PSO,which is guaranteed to keep the diversity of the particle swarm.When particles lose activity,this paper uses a special mutation or perturbation to activate particles and to make particles explore the search space more efficiently.Four Benchmark functions are selected as the test functions.The experimental results show that the IPSO can not only significantly speed up the convergence,but also effectively solve the premature convergence problem. |
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| Keywords: | Particle Swarm Optimization(PSO) Improved Particle Swarm Optimization(IPSO) evolutionary computation |
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