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research-article

SamACO: variable sampling ant colony optimization algorithm for continuous optimization

Authors:

Henry Shu-Hung Chung,

Ou LiuAuthors Info & Claims

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Volume 40, Issue 6

Pages 1555 - 1566

https://doi.org/10.1109/TSMCB.2010.2043094

Published: 01 December 2010 Publication History

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.

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Lu MXu BNener BCong JShi J(2022)An accurate cell tracking approach with self-regulated foraging behavior of ant colonies in dynamic microscopy imagesApplied Intelligence10.1007/s10489-021-02424-052:2(1448-1460)Online publication date: 1-Jan-2022
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SamACO: variable sampling ant colony optimization algorithm for continuous optimization
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Published In

cover image IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics Volume 40, Issue 6

December 2010

224 pages

ISSN:1083-4419

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Copyright © 2010.

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IEEE Press

Publication History

Published: 01 December 2010

Accepted: 03 February 2010

Revised: 05 July 2009

Received: 20 February 2009

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Cited By

Wu XZhou WFei MDu YZhou H(2024)Banyan tree growth optimization and applicationCluster Computing10.1007/s10586-022-03953-027:1(411-441)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10586-022-03953-0
Lu MXu BNener BCong JShi J(2022)An accurate cell tracking approach with self-regulated foraging behavior of ant colonies in dynamic microscopy imagesApplied Intelligence10.1007/s10489-021-02424-052:2(1448-1460)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s10489-021-02424-0
Ji JYu WZhang J(2019)Solving Nonlinear Equation Systems Using Multiobjective Differential EvolutionEvolutionary Multi-Criterion Optimization10.1007/978-3-030-12598-1_12(139-150)Online publication date: 10-Mar-2019
https://dl.acm.org/doi/10.1007/978-3-030-12598-1_12
Yang QChen WYu ZGu TLi YZhang HZhang J(2017)Adaptive Multimodal Continuous Ant Colony OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2016.259106421:2(191-205)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1109/TEVC.2016.2591064
Liu JLi PXiaoning Ma Jiaxun Xie (2017)Modeling, analysis and simulation on searching for global optimum region of particle swarm optimization2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969393(813-821)Online publication date: 5-Jun-2017
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Chen ZWang R(2017)Ant colony optimization with different crossover schemes for global optimizationCluster Computing10.1007/s10586-017-0793-820:2(1247-1257)Online publication date: 1-Jun-2017
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Moganarangan NBabukarthik RBhuvaneswari SSaleem Basha MDhavachelvan P(2016)A novel algorithm for reducing energy-consumption in cloud computing environmentJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2014.04.00728:1(55-67)Online publication date: 1-Jan-2016
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S ERamu PSwaminathan R(2016)Imperceptibility-Robustness tradeoff studies for ECG steganography using Continuous Ant Colony OptimizationExpert Systems with Applications: An International Journal10.1016/j.eswa.2015.12.01049:C(123-135)Online publication date: 1-May-2016
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