An interactive preference-weight genetic algorithm for multi-criterion satisficing optimization

Authors:
Ye Tao;Hong-Zhong Huang;Bo Yang
Affiliations:
Key Lab. for Precision and Non-traditional Machining Technol. of Ministry of Education, Dalian University of Technology, Dalian, P.R. China;School of Mechatronics Eng., University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China;School of Mechatronics Eng., University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

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Abstract

After review of several weight approaches, this paper proposes an interactive preference-weight method for Genetic Algorithm (GA). Decision makers (DMs) can pre-select a few feasible solutions, arrange these sample points based on their binary relations, and then design satisfaction degree ratio as the adaptive feasible regions. Through minimizing the weighted Lp-norm of the most satisfactory and unsatisfactory points, DMs can obtain inaccurate weight information for multi-criterion satisficing optimization in current population, and use it to formulate evaluation function as the preferred optimization direction for Pareto GA. Finally, DMs can acquire the corresponding optimal satisficing solution. The optimization of two-bar plane truss is used as an example to illustrate the proposed method.