作者机构：
Glorious Sun School of Business and Management, Donghua University, Shanghai, 200051, China;School 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, Shanghai, China
关键词：
cold chain logistics;multi-objective;location inventory routing problem （ LIRP ）;non-dominated sorting in genetic algorithm Ⅱ（ NSGA-Ⅱ ）
摘要：
To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.
期刊：
Revista de la Facultad de Ingeniería Universidad Central de Venezuela,2016年31(5):89-99 ISSN：0798-4065
作者机构：
[Zheng, Jianguo; Li, Kang] School of Management, Donghua University, Shanghai, 200051, China;[Wu, Daqing] School of Computer Science and Technology, University of South China, Hengyang, 421001, China
摘要：
In order to solve cold logistics network problem under uncertain demand environment, this paper proposes a novel location inventory routing model to optimize costs in cold logistics. The goal of the proposed model is to determine the inventory strategy, numbers of location facilities and vehicle routing decisions. A new discrete particle swarm optimization (DPSO) is introduced to solve this integrated model. Its performance is tested over a real case for the proposed problems. Results indicate that it is considerably efficient and effective to solve the problem.
期刊：
International Journal of Simulation Modelling,2016年15(4):742-753 ISSN：1726-4529
通讯作者：
Wu, D. Q.
作者机构：
[Wu, D. Q.] Shanghai Ocean Univ, Coll Econ & Management, Shanghai 201306, Peoples R China.;[Li, H. Y.; Wu, D. Q.] Univ South China, Comp Sci & Technol Inst, Hengyang 421001, Peoples R China.;[Dong, M.; Wu, D. Q.] Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Shanghai 200240, Peoples R China.;[Wu, D. Q.] Sichuan Univ Sci & Engn, Artificial Intelligence Key Lab Sichuan Prov, Zigong 643000, Peoples R China.;[Wu, D. Q.] Guangxi Univ Nationalities, Key Lab, Guangxi High Sch Complex Syst, Nanning 530004, Peoples R China.
通讯机构：
[Wu, D. Q.] S;[Wu, D. Q.] U;[Wu, D. Q.] G;[Wu, D. Q.] N;Shanghai Ocean Univ, Coll Econ & Management, Shanghai 201306, Peoples R China.
关键词：
Multi-Objective Optimization;Discrete Particle Swarm Optimization;Variable Neighbourhood Search;Vehicle Routing Problem with Time Windows
摘要：
This paper introduces a novel multi-objective algorithm (HMPSO) based on discrete particle swarm optimization (PSO) to solve vehicle routing problems with time windows (VRPTW). The presented HMPSO algorithm was combined with an advanced discrete PSO based on set and variable neighbourhood searches to find Pareto optimal routing solutions. These consisted of a complete routing schedule for serving the customers to minimize the two aims of travelling distance and number of vehicles. To increase the discrete PSO efficiency, a novel decoding scheme based on set was designed, and the variable neighbourhood local search was employed to explore new solutions. The experiment results were showed for a set of the Solomon's 56 VRPTW. The HMPSO algorithm was compared with some algorithms published in papers with the computational evaluations clearly supporting the high performance of the proposed HMPSO algorithm against other algorithms, and confirming that the HMPSO is an efficient algorithm because of a reasonable computational time and cost in solve VRPTW.
期刊：
Advances in modelling and analysis. A, general mathematical and computer tools,2016年53(1):145-159 ISSN：1258-5769
通讯作者：
Tang, Lixiang(Tanglx0731@126.com)
作者机构：
[Li, Haiyan; Ouyang, LiJun; Wu, Daqing] Computer Science and Technology Institute, University of South China, Hangyang, Hunan, China;[Tang, Lixiang] Department of Business Administration, Hunan University of Finance and Economics, Hunan, 410205, China;[Wu, Daqing] Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui Province, 230039, China;[Wu, Daqing] Key Laboratory of Guangxi High Schools for Complex System and Computational Intelligence, Guangxi University for Nationalities, Nanning, 530006, China;[Wu, Daqing] Artificial Intelligence Key Laboratory of Sichuan Province (Sichuan University of Science and Engineering), Zigong, 643000, China
通讯机构：
[Tang, L.] D;Department of Business Administration, Hunan University of Finance and Economics, Hunan, China
关键词：
Diversity;Multi-objective optimization;Particle swarm optimizer;Two local best solutions
期刊：
Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015,2015年:1975-1979 ISSN：1948-9439
通讯作者：
Wu, Daqing
作者机构：
[Wu, Daqing] Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Anhui, Peoples R China.;[Liu, Li; Gong, XiangJian; Wu, Daqing] Univ South China, Comp Sci & Technol Inst, Hengyang 421001, Hunan, Peoples R China.;[Deng, Li; Wu, Daqing] DongHua Univ, Glorious Sun Sch Business & Management, Shanghai 200051, Peoples R China.
通讯机构：
[Wu, Daqing] A;Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Anhui, Peoples R China.
会议名称：
27th Chinese Control and Decision Conference (CCDC)
会议时间：
MAY 23-25, 2015
会议地点：
Qingdao, PEOPLES R CHINA
会议主办单位：
[Wu, Daqing] Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Anhui, Peoples R China.^[Wu, Daqing;Liu, Li;Gong, XiangJian] Univ South China, Comp Sci & Technol Inst, Hengyang 421001, Hunan, Peoples R China.^[Wu, Daqing;Deng, Li] DongHua Univ, Glorious Sun Sch Business & Management, Shanghai 200051, Peoples R China.
会议论文集名称：
Chinese Control and Decision Conference
关键词：
Multi-objective Optimization;Particle Swarm Optimizer;Neighborhood Best Particle;Dynamic Swamis;Economic Environmental Dispatch
摘要：
An efficient co-evolutionary multi-objective particle swarm optimizer named ECMPSO was proposed.ECMPSO uses dynamic multiple swarms to deal with multiple objectives,taking one objective is optimized b
作者机构：
[张力; 青涛; 蒋建军; 李鹏程; 王以群] Human Factors Institute, School of Economic &, Management, University of South China, Hengyang, China;[张晓玲; 伍大清] School of Computer Science and Technique, University of South China, Hengyang, China;[李敏] Networks Center, University of South China, Hengyang, China;[彭玉元] School of Computer Engineering, Guangzhou College of South China University of Technology, Guangzhou, China;[张力] Hunan Institute of Technology, Hengyang, China
