This paper proposes a simple but promising clustering-based multi-objective evolutionary algorithm, termed as CMOEA. At each generation, CMOEA first divides the current population into several subpopulations by Gaussian mixture clustering. To generate offsprings, the search stage, either exploration or exploitation, is determined by the relative difference between the subpopulations’ hypervolumes of two adjacent generations. CMOEA selects the parents from different subpopulations in case of exploration stage, and from the same subpopulation in case of exploitation stage. In the environmental selection phase, the hypervolume indicator is used to update the population. Simulation experiments on nine multi-objective problems show that CMOEA is competitive with five popular multi-objective evolutionary algorithms.
CITATION STYLE
Zheng, W., Wu, J., Zhang, C., & Sun, J. (2020). A Clustering-Based Multiobjective Evolutionary Algorithm for Balancing Exploration and Exploitation. In Communications in Computer and Information Science (Vol. 1159 CCIS, pp. 355–369). Springer. https://doi.org/10.1007/978-981-15-3425-6_28
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