SamACO: variable sampling ant colony optimization algorithm for continuous optimization

  • Authors:

  • Xiao-Min Hu;Jun Zhang;Henry Shu-Hung Chung;Yun Li;Ou Liu

  • Affiliations:

  • Department of Computer Science, Sun Yat-Sen University, Guangzhou, China and Key Laboratory of Digital Life, Sun Yat-Sen University, Ministry of Education, Guangzhou, China;Department of Computer Science, Sun Yat-Sen University, Guangzhou, China and Key Laboratory of Digital Life, Sun Yat-Sen University, Ministry of Education, Guangzhou, China;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong;Department of Electronics and Electrical Engineering, University of Glasgow, Glasgow, UK;School of Accounting and Finance, Hong Kong Polytechnic University, Kowloon, Hong Kong

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
  • Year:
  • 2010

Quantified Score

Hi-index 0.01

Visualization

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.