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 L p -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.
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Tao, Y., Huang, HZ., Yang, B. (2006). An Interactive Preference-Weight Genetic Algorithm for Multi-criterion Satisficing Optimization. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_88
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DOI: https://doi.org/10.1007/11881070_88
Publisher Name: Springer, Berlin, Heidelberg
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