Abstract
In the evolutionary multi-objective optimization, keeping a good balance between convergence and diversity is particularly important to the performance of evolutionary algorithms. In addition, it is becoming more and more important to bring into user preferences, as the number of targets increases, the possibility of using a limited population to achieve a representative subset of the Pareto optimal solution will be reduced. A multi-objective optimization evolutionary algorithm guided by reference vector is proposed. The reference vector can not only be used to decompose the original multi-objective optimization problem into several single-objective subproblems, but also can be used to clarify user preferences for a preference subset in the whole Pareto frontier.
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Zhao, Q., Zhang, L. (2021). Based on Preference Information Optimization of Multi-objective Particle Swarm Optimization. In: Sugumaran, V., Xu, Z., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2021. Advances in Intelligent Systems and Computing, vol 1384. Springer, Cham. https://doi.org/10.1007/978-3-030-74811-1_105
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DOI: https://doi.org/10.1007/978-3-030-74811-1_105
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