Abstract
In this paper a new evolutionary algorithm is described for multi-objective optimization. The new method handles non-linear objective functions and constraints and supports the decision-maker with an estimation of the Pareto set. This cluster-based method applies the Pareto-dominance principle. It approximates the Pareto set with the prototypes for each cluster and alternative prototypes as secondary population. The non-dominated set is continuously being up-dated: based on the Pareto ranking, the poorest clusters are regularly deleted, and the new ones are set.
The method solves the usual test problems with a satisfactory level of accuracy.
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Borgulya, I. (2001). A Cluster-Based Evolutionary Algorithm for Multi-objective Optimization. In: Reusch, B. (eds) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. Lecture Notes in Computer Science, vol 2206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45493-4_38
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DOI: https://doi.org/10.1007/3-540-45493-4_38
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