TY - JOUR
T1 - SamACO
T2 - Variable sampling ant colony optimization algorithm for continuous optimization
AU - Hu, Xiao Min
AU - Zhang, Jun
AU - Chung, Henry Shu Hung
AU - Li, Yun
AU - Liu, Ou
N1 - Funding Information:
Manuscript received February 20, 2009; revised July 5, 2009, November 2, 2009, and January 29, 2010; accepted February 3, 2010. Date of publication April 5, 2010; date of current version November 17, 2010. This work was supported in part by the National Natural Science Foundation of China Joint Fund with Guangdong under Key Project U0835002, by the National High-Technology Research and Development Program (“863” Program) of China 2009AA01Z208, and by the Sun Yat-Sen Innovative Talents Cultivation Program for Excellent Tutors 35000-3126202. This paper was recommended by Associate Editor H. Ishibuchi.
PY - 2010/12
Y1 - 2010/12
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
C2 - 20371409
AN - SCOPUS:78649938831
SN - 1083-4419
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
IS - 6
M1 - 5443623
ER -