caa a22 4500 445842555 CHVBK 20180317145349.0 cr unu---uuuuu 170323e20110301xx s 000 0 eng 10.1007/s11047-010-9192-2 doi (NATIONALLICENCE)springer-10.1007/s11047-010-9192-2 An improved multi-agent genetic algorithm for numerical optimization [Elektronische Daten] [Xiaoying Pan, Licheng Jiao, Fang Liu] Multi-agent genetic algorithm (MAGA) is a good algorithm for global numerical optimization. It exploited the known characteristics of some benchmark functions to achieve outstanding results. But for some novel composition functions, the performance of the MAGA significantly deteriorates when the relative positions of the variables at the global optimal point are shifted with respect to the search ranges. To this question, an improved multi-agent genetic algorithm for numerical optimization (IMAGA) is proposed. IMAGA make use of the agent evolutionary framework, and constructs heuristic search and a hybrid crossover strategy to complete the competition and cooperation of agents, a convex mutation operator and some local search to achieve the self-learning characteristic. Using the theorem of Markov chain, the improved multi-agent genetic algorithm is proved to be convergent. Experiments are conducted on some benchmark functions and composition functions. The results demonstrate good performance of the IMAGA in solving complicated composition functions compared with some existing algorithms. Springer Science+Business Media B.V., 2010 Competition nationallicence Cooperation nationallicence Multi-agent system nationallicence Mutation nationallicence Self-learning nationallicence Pan Xiaoying School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, 710121, Xi'an, China aut Jiao Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, 710071, Xi'an, China aut Liu Fang School of Computer Science and Engineering, Xidian University, 710071, Xi'an, China aut Natural Computing Springer Netherlands 10/1(2011-03-01), 487-506 1567-7818 10:1<487 2011 10 11047 https://doi.org/10.1007/s11047-010-9192-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11047-010-9192-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Pan Xiaoying School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, 710121, Xi'an, China aut NATIONALLICENCE 700 1- Jiao Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, 710071, Xi'an, China aut NATIONALLICENCE 700 1- Liu Fang School of Computer Science and Engineering, Xidian University, 710071, Xi'an, China aut NATIONALLICENCE 773 0- Natural Computing Springer Netherlands 10/1(2011-03-01), 487-506 1567-7818 10:1<487 2011 10 11047 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer