Analysis and Comparison Among Ant System; Ant Colony System and Max-Min Ant System With Different Parameters Setting

被引：0
|
作者：
Jangra, Renu [1 ]
Kait, Ramesh [1 ]
机构：
[1] Kurukshetra Univ, Dept Comp Sci & Applicat, Kurukshetra, Haryana, India
来源：
2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE & COMMUNICATION TECHNOLOGY (CICT) | 2017年
关键词：
Ant Colony Optimization; Ant System; Ant Colony System; Max-Min Ant System; OPTIMIZATION;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Ant colony optimization is a victorious technique to resolve complicated combinatorial optimized problems. Subsequent Ant System; the primary ACO algorithm, the tremendous number of algorithmic variations have been deployed that illustrate appreciably superior performance on a broad variety of optimized problems. In this paper, we present the basic modifications in Ant Colony System - ACS and Max-Min Ant System - MMAS with respect to Ant System - AS. Also, compare the result of these three algorithms based on different parameter adjustments.
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页数：4
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