Binary-coding-based ant colony optimization and its convergence

• Published in: Journal of computer science and technology Vol. 19; no. 4; pp. 472 - 478
• Main Authors: Bu, Tian-Ming, Yu, Song-Nian, Guan, Hui-Wei
• Format: Journal Article
• Language: English
• Published: Beijing Springer Nature B.V 01.07.2004; School of Computer Engineering and Science,Shanghai University,Shanhai 200072,P.R.China%Department of Computer Science,North Shore Community College,MA 01923,USA
• Subjects: Algorithms; Ant colony optimization; Binary codes; Colonies; Combinatorial analysis; Convergence; Genetic algorithms; Genetics; Mathematical analysis; Optimization; Positive feedback; Studies; genetic algorithm; heuristic; function optimization; ant colony optimization; convergence; binary-coding
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/BF02944748

Ant colony optimization (ACO for short) is a meta-heuristics for hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. In this paper, genetic algorithm's (GA for short) ideas are introduced into ACO to present a new binary-coding based ant colony optimization. Compared with the typical ACO, the algorithm is intended to replace the problem's parameter-space with coding-space, which links ACO with GA so that the fruits of GA can be applied to ACO directly. Furthermore, it can not only solve general combinatorial optimization problems, but also other problems such as function optimization. Based on the algorithm, it is proved that if the pheromone remainder factor ρ is under the condition of ρ≥1, the algorithm can promise to converge at the optimal, whereas if 0<ρ<1, it does not.