| 用并行化的QPSO解决有约束的优化问题 |
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| 引用本文： | 马艳,须文波,孙俊,刘阳. 用并行化的QPSO解决有约束的优化问题[J]. 计算机应用, 2006, 26(9): 2047-2050 |
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| 作者姓名： | 马艳 须文波 孙俊 刘阳 |
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| 作者单位： | 江南大学,信息工程学院,江苏,无锡,214122;曲阜师范大学,计算机科学学院,山东,曲阜,273165 |
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| 摘 要： | 采用粒子群系统的并行化的量子化模型提高全局搜寻能力，在解决约束问题时采用不固定的多阶段任务补偿函数以提高收敛性，并获得更准确的结果，提出了并行化的QPSO（PQPSO）算法。此算法在几个可信赖的基准函数中被测试，并且实验结果显示PQPSO的最优值和运行时间比QPSO和传统的PSO有很大的提高，而且运行所用的时间资源接近线性减少。
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| 关 键 词： | 并行化 量子化粒子群优化算法 约束优化 |
| 文章编号： | 1001-9081（2006）09-2047-4 |
| 收稿时间： | 2006-03-27 |
| 修稿时间： | 2006-03-272006-06-21 |
Solving constrained optimization problems with parallel quantum particle swarm optimization |
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| MA Yan,XU Wen-bo,SUN Jun,LIU Yang. Solving constrained optimization problems with parallel quantum particle swarm optimization[J]. Journal of Computer Applications, 2006, 26(9): 2047-2050 |
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| Authors: | MA Yan XU Wen-bo SUN Jun LIU Yang |
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| Affiliation: | 1. School of Information Technology, Southern Yangtze University, Wuxi Jiangsu 214122, China; 2. College of Computer Science, Qufu Normal University, Qufu Shandong 273165, China |
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| Abstract: | In this paper,parallel quantum model of particle swarm system was adopted to enhance the global search ability,and as well as a non-stationary multi-stage assignment penalty in solving constrained problem to improve the convergence and gain more accurate results.Thus,a new optimization algorithm of PQPSO was proposed.This approach was tested on several accredited benchmark functions and the experimental results show much advantage of PQPSO to QPSO(Quantum-behaved Particle Swarm Optimization) and the traditional PSO in terms of optimal value and running time,and the running time is also decreased in linear. |
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| Keywords: | parallel QPSO(Quantum-behaved Particle Swarm Optimization) constrained optimization |
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