Uniform Big Bang-Chaotic Big Crunch optimization

被引：47

作者：

Alatas, Bilal ^{[1
]}

机构：

[1] Tunceli Univ, Dept Comp Engn, Fac Engn, Tunceli, Turkey

来源：

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2011年 / 16卷 / 09期

关键词：

Big Bang-Big Crunch optimization; Chaos; Metaheuristic optimization; Soft computing; Uniform population; SWARM OPTIMIZATION; ALGORITHM; DESIGN;

D O I：

10.1016/j.cnsns.2010.12.025

中图分类号：

O29 [应用数学];

学科分类号：

070104 [应用数学];

摘要：

This study proposes methods to improve the convergence of the novel optimization method, Big Bang-Big Crunch (BB-BC). Uniform population method has been used to generate uniformly distributed random points in the Big Bang phase. Chaos has been utilized to rapidly shrink those points to a single representative point via a center of mass in the Big Crunch phase. The proposed algorithm has been named as Uniform Big Bang-Chaotic Big Crunch (UBB-CBC). The performance of the UBB-CBC optimization algorithm demonstrates superiority over the BB-BC optimization for the benchmark functions. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：3696 / 3703

页数：8

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