Search Property of Nonlinear Map Optimization
Search Property of Nonlinear Map Optimization
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DOI：
10.1109/cec.2019.8790227
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
K. Jin'no;Tomoyuki Sasaki;H. Nakano
中科院分区：
文献类型：
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作者：
K. Jin'no;Tomoyuki Sasaki;H. Nakano
我们提出了非线性地图优化（NMO），以提高粒子群优化（PSO）算法的溶液搜索能力。 NMO是基于PSO的群体智能算法之一。我们先前提出了一个规范确定性系统PSO（CD-PSO），该系统已从PSO中删除，以分析其解决方案搜索行为，并仅提取PSO解决方案搜索属性的基本动力学。尽管CD-PSO的动力学描述了PSO的基本动力学，但CD-PSO的溶液搜索性能比PSO差。原因之一是搜索点的分布。尽管PSO的搜索点分布取决于随机因素的正态分布，但确定性系统的CD-PSO的搜索点分布与正态分布不同。为了改善CD-PSO的搜索点分布，我们提出了修改的CD-PSO。基于修改后的CD-PSO，我们提出了一种NMO算法，其搜索点由非线性映射得出。在本文中，我们澄清了NMO的解决方案搜索属性。搜索点的分布类似于正态分布形状。同样，由于非线性取决于参数，因此NMO的非线性映射得出了复杂的行为。这些属性导致NMO的局部搜索能力，与PSO相比，它得到了改善。同样，类似于PSO的群体中的信息交换与全球搜索能力有关。由于NMO可以分别考虑本地搜索和全局搜索，因此与常规PSO相比，可以通过单峰功能提高解决方案搜索能力。
We proposed Nonlinear Map Optimization (NMO) to improve the solution search capability of particle swarm optimization (PSO) algorithm. NMO is one of PSO-based swarm intelligence algorithms. We have previously proposed a canonical deterministic system PSO (CD-PSO) that is removed probabilistic factors from PSO to analyze its solution search behavior and is extracted only the essential dynamics of the solution search property of PSO. Although the dynamics of CD-PSO describe the basic dynamics of PSO, the solution search performance of CD-PSO is very poor than PSO. One of the causes is the distribution of search points. Although the search point distribution of PSO has a normal distribution shape depending on stochastic factors, the search point distribution of CD-PSO which is a deterministic system is not similar to the normal distribution. In order to improve the search point distribution of CD-PSO, we proposed a modified CD-PSO. Based on the modified CD-PSO, we proposed an NMO algorithm whose search points are derived by a nonlinear map. In this article, we clarify that the solution search property of NMO. The distribution of search points is similar to a normal distribution shape. Also, the nonlinear mapping of NMO derives complex behavior due to nonlinearity depended on parameters. These properties lead to the local search capability of NMO which is improved compared to PSO. Also, information exchange within the swarm similar to PSO is related to the global search ability. Since NMO can consider local search and global search separately, the solution search capability can be improved with unimodal functions than conventional PSO.