| 改进的粒子群算法及在结构优化中的应用 |
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| 引用本文： | 吴军,李为吉.改进的粒子群算法及在结构优化中的应用[J].陕西理工学院学报(自然科学版),2006,22(4):36-39. |
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| 作者姓名： | 吴军 李为吉 |
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| 作者单位： | 西北工业大学,航空学院,陕西,西安,710072 |
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| 摘 要： | 粒子群算法（PSO）是一种有效的演化算法。将免疫算法中浓度的概念引入粒子群算法中，提出了一种基于浓度概念的竞争排挤粒子群算法；并提出了基于样本方差的种群多样性指标，用以定量的描述种群多样性。这种改进的粒子群算法增加了粒子群的种群多样性，提高了算法的全局搜索能力。最后将本文的算法应用于梁结构和桁架结构优化设计，验证了算法的有效性。
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| 关 键 词： | 粒子群算法 演化计算 结构优化设计 |
| 文章编号： | 1673-2944(2006)04-0036-04 |
| 收稿时间： | 2006-07-28 |
| 修稿时间： | 2006年7月28日 |
Improved particle swarm optimization and its application to structural optimum design |
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| WU Jun,LI Wei-ji.Improved particle swarm optimization and its application to structural optimum design[J].Journal of Shananxi University of Technology:Natural Science Edition,2006,22(4):36-39. |
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| Authors: | WU Jun LI Wei-ji |
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| Abstract: | Particle swarm optimization(PSO) algorithm is an effective evolutionary algorithm.In this paper,the competitive particle swarm optimization algorithm(CPSO) was developed.The antibody density in artificial immune algorithm was introduced into PSO to improve searching characteristics by keeping the diversity of the population.A parameter was also defined to describe the diversity mathematically.The CPSO algorithm was then applied to beam structural design and truss structural design.The result shows that the CPSO acted efficiently. |
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| Keywords: | particle swarm optimization evolutionary computation structural optimum design |
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