Ant colony optimization for continuous functions by using novel pheromone updating

被引：38

作者：

Seckiner, Serap Ulusam ^{[1
]}

Eroglu, Yunus ^{[1
]}

Emrullah, Merve ^{[1
]}

Dereli, Turkay ^{[1
]}

机构：

[1] Gaziantep Univ, Fac Engn, Dept Ind Engn, TR-27310 Sehitkamil, Gaziantep, Turkey

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2013年 / 219卷 / 09期

关键词：

Ant colony optimization; Continuous optimization; Novel pheromone updating; Global minimum; Comparative analysis; RANDOM SEARCH TECHNIQUE; HEURISTIC APPROACH; GENETIC ALGORITHM; GLOBAL MINIMUM;

D O I：

10.1016/j.amc.2012.10.097

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper presents an ant colony optimization (ACO) algorithm for continuous functions based on novel pheromone updating. At the end of the each iteration in the proposed algorithm, pheromone is updated according to percentiles which determine the number of ants to track the best candidate solution. It is performed by means of solution archive and information provided by previous solutions. Performance of the proposed algorithm is tested on ten benchmark problems found in the literature and compared with performances of previous methods. The results show that ACO which is based on novel pheromone updating scheme (ACO-NPU) handles different types of continuous functions very well and can be a robust alternative approach to other stochastic search algorithms. (C) 2012 Elsevier Inc. All rights reserved.

引用

页码：4163 / 4175

页数：13

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