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Preference-Based Evolutionary Multi-objective Optimization

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2 Author(s)
Zhenhua Li ; Sch. of Appl. Math., Guangdong Univ. of Technol., Guangzhou, China ; Hai-Lin Liu

Evolutionary Multi-objective Optimization (EMO) approaches have been amply applied to find a representative set of Pareto-optimal solutions in the past decades. Although there are advantages of getting the range of each objective and the shape of the entire Pareto front for an adequate decision-making, the task of choosing a preferred set of Pareto-optimal solutions is also important. In this paper, we combine a preference-based strategy with an EMO methodology and demonstrate how, instead of one solution, a preferred set of solutions in the preferred range can be found. The basic idea is that each objective function corresponds to a marginal utility function, which indicates the decision-maker's preferred range for each objective. The corresponding utility function denotes the decision-maker's satisfaction. Such procedures will provide the decision-maker with a set of solutions near his preferred ranges so that a better and more reliable decision can be made.

Published in:

Computational Intelligence and Security (CIS), 2012 Eighth International Conference on

Date of Conference:

17-18 Nov. 2012