An Improved Particle Swarm Optimization Algorithm
An Improved Particle Swarm Optimization Algorithm
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DOI：
10.4028/www.scientific.net/amr.850-851.809
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发表时间：
2013-12
期刊：
影响因子：
--
通讯作者：
H. Ni;Wei Wang
中科院分区：
文献类型：
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作者：
H. Ni;Wei Wang
小生境是多峰函数优化的一种重要方法。针对粒子群优化（PSO）算法应用于多峰函数优化时存在易陷入早熟、收敛速度慢等问题，提出了一种基于小生境技术的改进PSO算法。粒子群算法利用群体行为的特性，快速求解优化问题。小生境技术具有在多模态域中定位多个解的能力。改进后的粒子群算法不仅具有高效的并行性，而且由于引入了小生境技术，增加了种群的多样性。仿真结果表明，新算法优于传统的粒子群算法，具有更强的适应性和收敛性，较好地解决了多峰函数优化问题。
Niche is an important technique for multi-peak function optimization. When the particle swarm optimization (PSO) algorithm is used in multi-peak function optimization, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, an improved PSO algorithm based on niche technique is brought forward. PSO algorithm utilizes properties of swarm behavior to solve optimization problems rapidly. Niche techniques have the ability to locate multiple solutions in multimodal domains. The improved PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the niche technique. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on multi-peak function optimization.