作者机构：
[刘立; 伍大清] Computer Science and Technology Institute, University of South China, Hengyang, China;[伍大清] Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing, China;[伍大清] Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, China;[郑建国; 朱君璇; 伍大清; 赵燕] School of Business and Management, Donghua University, Shanghai, China;[伍大清] Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Province, Chengdu University, Chengdu, China
作者机构：
[伍大清; 郑建国] Glorious Sun School of Business and Management, Donghua University, Shanghai , China;[周亮] Shanghai Lixin University of Commerce, Shanghai , China;[伍大清] Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong , China;[伍大清] Computer Science and Technology Institute, University of South China, Hengyang , China
通讯机构：
Glorious Sun School of Business and Management, Donghua University, Shanghai, China
摘要：
The traveling salesman problem（ TSP） is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization（ DMPSO-ACO） was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer（ DMPSO） and the stochastic exploitation of the ant colony optimization（ ACO） for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.
作者机构：
[郑建国; 伍大清] Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China;[伍大清] Artificial Intelligence Key Laboratory of Sichuan Province, Zigong 643000, China;[伍大清] Department of Computer Science and Technology, University of South China, Hengyang 421001, China
通讯机构：
Glorious Sun School of Business and Management, Donghua University, China
期刊：
Discrete Dynamics in Nature and Society,2012年2012(Pt.4):578064-1-578064-22 ISSN：1026-0226
通讯作者：
Wu, Daqing
作者机构：
[Zheng, Jianguo; Wu, Daqing] DongHua Univ, Glorious Sun Sch Business & Management, Shanghai 200051, Peoples R China.;[Wu, Daqing] Univ S China, Comp Sci & Technol Inst, Hengyang 421001, Hunan, Peoples R China.;[Wu, Daqing] Sichuan Univ Sci & Engn, Artificial Intelligence Key Lab Sichuan Prov, Zigong 643000, Peoples R China.
通讯机构：
[Wu, Daqing] D;DongHua Univ, Glorious Sun Sch Business & Management, Shanghai 200051, Peoples R China.
摘要：
A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.
