A Particle Swarm Optimization Using Local Stochastic Search for Continuous Optimization
A Particle Swarm Optimization Using Local Stochastic Search for Continuous Optimization
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DOI：
10.1007/978-3-642-31837-5_8
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发表时间：
2012-07
期刊：
影响因子：
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通讯作者：
Jianli Ding;Jin Liu;Yun Wang;Wensheng Zhang;Wenyong Dong
中科院分区：
文献类型：
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作者：
Jianli Ding;Jin Liu;Yun Wang;Wensheng Zhang;Wenyong Dong
粒子群优化算法（PSO）是一种基于群体智能的启发式优化技术，可以应用于广泛的问题。在分析传统粒子群算法的动力学基础上，提出了一种基于局部随机搜索策略的改进粒子群算法（LSSPSO）。这是受到一种社会现象的启发，即每个人都想先超过最近的上级，然后再超过所有的上级。具体而言，LSSPSO采用局部随机搜索来调整惯性权重，在多样性和收敛速度之间保持平衡，以提高传统PSO的性能。在单峰和多峰测试函数上进行的实验证明了LSSPSO在解决多个基准问题时的有效性，与其他几种PSO变体相比。
The particle swarm optimizer (PSO) is a swarm intelligence based heuristic optimization technique that can be applied to a wide range of problems. After analyzing the dynamics of tranditioal PSO, this paper presents a new PSO variant based on local stochastic search strategy (LSSPSO) for performance enhancement. This is inspired by a social phenomenon that everyone wants to first exceed the nearest superior and then all superior. Specifically, LSSPSO adopts a local stochastic search to adjust inertia weight in terms of keeping a balance between the diversity and the convergence speed, aiming to improve the performance of tranditioal PSO. Experiments conducted on unimodal and multimodal test functions demonstrate the effectiveness of LSSPSO in solving multiple benchmark problems as compared to several other PSO variants.