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.

Publication DOI: https://doi.org/10.1109/TSMCB.2010.2043094
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Ant algorithm,ant colony optimization (ACO),ant colony system (ACS),continuous optimization,function optimization,local search,numerical optimization,Control and Systems Engineering,Software,Information Systems,Human-Computer Interaction,Computer Science Applications,Electrical and Electronic Engineering
Publication ISSN: 1941-0492
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://ieeexpl ... ocument/5443623 (Publisher URL)
PURE Output Type: Article
Published Date: 2010-12-01
Authors: Hu, Xiao Min
Zhang, Jun
Chung, Henry Shu Hung
Li, Yun
Liu, Ou (ORCID Profile 0000-0001-9480-8729)

Download

[img]

Version: Accepted Version

| Preview

Export / Share Citation


Statistics

Additional statistics for this record