A Fast Particle Swarm Optimization Algorithm with Cauchy Mutation and Natural Selection Strategy

机译：具有柯西突变和自然选择策略的快速粒子群优化算法

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The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in which each particle studies its own previous best solution and the group's previous best to optimize problems. One problem exists in PSO is its tendency of trapping into local optima. In this paper, a fast particle swarm optimization (FPSO) algorithm is proposed by combining PSO and the Cauchy mutation and an evolutionary selection strategy. The idea is to introduce the Cauchy mutation into PSO in the hope of preventing PSO from trapping into a local optimum through long jumps made by the Cauchy mutation. FPSO has been compared with another improved PSO called AMPSO [12] on a set of benchmark functions. The results show that FPSO is much faster than AMPSO on all the test functions.

机译：标准粒子群优化（PSO）算法是一种新颖的进化算法，其中每个粒子都研究其自己先前的最佳解决方案，并研究该组先前的最佳解决方案。 PSO中存在的一个问题是其陷入局部最优状态的趋势。本文提出了一种结合粒子群算法和柯西突变的快速粒子群优化算法，并提出了一种进化选择策略。想法是将Cauchy突变引入PSO，以防止PSO通过Cauchy突变引起的跳远而陷入局部最优状态。 FPSO已在一组基准功能上与另一种改进的PSO（称为AMPSO [12]）进行了比较。结果表明，在所有测试功能上，FPSO比AMPSO快得多。

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来源
《International Symposium on Intelligence Computation amp; Applications(ISICA'2007); 20070921-23; Wuhan(CN)》|2007年|P.334-343|共10页
会议地点 Wuhan(CN)
作者
Changhe Li; Yong Liu; Aimin Zhou; Lishan Kang; Hui Wang;
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作者单位

China University of Geosciences, School of Computer, Wuhan, P.R. China, 430074;

The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan, 965-8580;

Department of Computer Science, University of Essex Wivenhoe Park, Colchester, CO4 3SQ;

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原文格式 PDF
正文语种 eng
中图分类人工智能理论;
关键词
particle swarm optimization; cauchy mutation; swarm intelligence;

机译：粒子群算法；柯西突变；群智能;

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4. A Fast Particle Swarm Optimization Algorithm with Cauchy Mutation and Natural Selection Strategy [C] . Changhe Li, Yong Liu, Aimin Zhou, International Symposium on Intelligence Computation Applications . 2007

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机译：基于高斯粒子群优化目标变量的柯西变异用于支持向量机参数选择

A Fast Particle Swarm Optimization Algorithm with Cauchy Mutation and Natural Selection Strategy

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