SamACO: variable sampling ant colony optimization algorithm for continuous optimization

Xiao Min Hu, Jun Zhang, Henry Shu Hung Chung, Yun Li, Ou Liu

Research output: Contribution to journalArticle

47 Citations (Scopus)

Abstract

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants' solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising.

LanguageEnglish
Article number5443623
Pages1555-1566
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume40
Issue number6
DOIs
Publication statusPublished - 1 Dec 2010

Fingerprint

Ant colony optimization
Sampling
Combinatorial optimization
Artificial intelligence

Keywords

  • ant algorithm
  • ant colony optimization (ACO)
  • ant colony system (ACS)
  • continuous optimization
  • function optimization
  • local search
  • numerical optimization

Cite this

Hu, Xiao Min ; Zhang, Jun ; Chung, Henry Shu Hung ; Li, Yun ; Liu, Ou. / SamACO : variable sampling ant colony optimization algorithm for continuous optimization. In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 2010 ; Vol. 40, No. 6. pp. 1555-1566.
@article{ab3c52313feb43dea729244bade64bd2,
title = "SamACO: variable sampling ant colony optimization algorithm for continuous optimization",
abstract = "An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants' solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising.",
keywords = "ant algorithm, ant colony optimization (ACO), ant colony system (ACS), continuous optimization, function optimization, local search, numerical optimization",
author = "Hu, {Xiao Min} and Jun Zhang and Chung, {Henry Shu Hung} and Yun Li and Ou Liu",
year = "2010",
month = "12",
day = "1",
doi = "10.1109/TSMCB.2010.2043094",
language = "English",
volume = "40",
pages = "1555--1566",
journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics",
issn = "1083-4419",
number = "6",

}

SamACO : variable sampling ant colony optimization algorithm for continuous optimization. / Hu, Xiao Min; Zhang, Jun; Chung, Henry Shu Hung; Li, Yun; Liu, Ou.

In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 40, No. 6, 5443623, 01.12.2010, p. 1555-1566.

Research output: Contribution to journalArticle

TY - JOUR

T1 - SamACO

T2 - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

AU - Hu, Xiao Min

AU - Zhang, Jun

AU - Chung, Henry Shu Hung

AU - Li, Yun

AU - Liu, Ou

PY - 2010/12/1

Y1 - 2010/12/1

N2 - An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants' solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising.

AB - An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants' solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising.

KW - ant algorithm

KW - ant colony optimization (ACO)

KW - ant colony system (ACS)

KW - continuous optimization

KW - function optimization

KW - local search

KW - numerical optimization

UR - http://www.scopus.com/inward/record.url?scp=78649938831&partnerID=8YFLogxK

U2 - 10.1109/TSMCB.2010.2043094

DO - 10.1109/TSMCB.2010.2043094

M3 - Article

VL - 40

SP - 1555

EP - 1566

JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

SN - 1083-4419

IS - 6

M1 - 5443623

ER -