A preference oriented two-layered multiagent evolutionary algorithm for multi-objective job shop problems

Authors:
Xinrui Duan;Jing Liu;Ruochen Liu;Licheng Jiao
Affiliations:
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

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Abstract

From the viewpoint of decision making process, it brings inconveniences for decision makers to select one (few) proper solution(s). Thus we propose preference oriented two-layered multiagent evolutionary algorithm (TL-MAEA) to meet customers' needs. The algorithm has a structure of two layers: in the top layer, preference relations among multiple objectives are calculated through interactions with the decision maker; while in the bottom layer, MAEA is employed to obtain the optimal solution corresponding to the preference relations. In the experimental, 12 benchmark problems are used to test the algorithm. The results show that the proposed algorithm is effective.