Abstract
Set-based evolutionary optimization based on the performance indicators is one of the effective methods to solve many-objective optimization problems; however, previous researches didn’t make full use of the preference information of a high-dimensional objective space to guide the evolution of a population. In this study, we propose a set-based many-objective evolutionary optimization algorithm guided by preferred regions. In the mode of set-based evolution, the proposed method dynamically determines a preferred region of the high-dimensional objective space, designs a selection strategy on sets by combining Pareto dominance relation on sets with the above preferred region, and develops a crossover operator on sets guided by the above preferred region to produce a Pareto front with superior performance. The proposed method is applied to four benchmark many-objective optimization problems, and the experimental results empirically demonstrate its effectiveness.
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Acknowledgements
This work was jointly supported by National Natural Science Foundation of China with grant No. 61375067 and 61403155, National Basic Research Program of China (973 Program) with grant No. 2014CB046306-2, and Natural Science Foundation of Jiangsu Province with grant No. BK2012566
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Gong, D., Sun, F., Sun, J., Sun, X. (2015). Set-Based Many-Objective Optimization Guided by Preferred Regions. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_10
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DOI: https://doi.org/10.1007/978-3-319-22053-6_10
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