This work investigates a method to search practically desirable solutions expanding the objective space with additional fitness functions associated to particular decision variables. The aim is to find solutions around preferred values of the chosen variables while searching for optimal solutions in the original objective space. Solutions to be practically desirable are constrained to be within a certain distance from the instantaneous Pareto optimal set computed in the original objective space. Our experimental results show that the proposed method can effectively find practically desirable solutions. © 2013 Springer-Verlag.
CITATION STYLE
Kusuno, N., Aguirre, H., Tanaka, K., & Koishi, M. (2013). Practically desirable solutions search on multi-objective optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7997 LNCS, pp. 438–443). https://doi.org/10.1007/978-3-642-44973-4_46
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