Model-based search for combinatorial optimization: A critical survey

被引：140
|
作者：
Zlochin, M [1 ]
Birattari, M
Meuleau, N
Dorigo, M
机构：
[1] Weizmann Inst Sci, Dept Appl Math & Comp Sci, IL-76100 Rehovot, Israel
[2] Free Univ Brussels, IRIDIA, Brussels, Belgium
[3] NASA, Ames Res Ctr, Moffett Field, CA 94035 USA
[4] Tech Univ Darmstadt, Intellektik, D-64287 Darmstadt, Germany
来源：
ANNALS OF OPERATIONS RESEARCH | 2004年 / 131卷 / 1-4期
关键词：
ant colony optimization; cross-entropy method; stochastic gradient ascent; estimation of distribution algorithms; adaptive optimization; metaheuristics;
D O I：
10.1023/B:ANOR.0000039526.52305.af
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
In this paper we introduce model-based search as a unifying framework accommodating some recently proposed metaheuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, cross-entropy and estimation of distribution methods. We discuss similarities as well as distinctive features of each method and we propose some extensions.
引用
收藏
页码：373 / 395
页数：23
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