Vector Evaluated Particle Swarm Optimization Exploration Behavior Part II: Quantitative Analysis

被引：0

作者：

Scheepers, Christiaan ^{[1
]}

Engelbrecht, Andries P. ^{[1
]}

机构：

[1] Univ Pretoria, Dept Comp Sci, ZA-0002 Pretoria, South Africa

来源：

2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2016年

关键词：

ALGORITHMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A quantitative analysis in low dimensional objective space of the exploration behavior of the vector evaluated particle swarm optimization (VEPSO) algorithm is presented. A previous study showed that the VEPSO algorithm continues to explore the objective space, and does not focus enough on exploitation. To improve exploitation, the multi guided VEPSO with random archive selection was introduced. In this paper a new quantitive measurement, that tracks the particles' movement diversity in decision space, is developed. The results reinforce the conclusions drawn in earlier research. Additionally, the movement diversity measurement provides additional insight into why one of the two MGVEPSOA swarms continue to explore more of the objective space when tested on the problems in the Zitzler, Deb and Thiele (ZDT) test set.

引用

页码：1855 / 1861

页数：7

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