Abstract
Many evolutionary algorithms have been proposed to deal with Constrained Optimization Problems (COPs). Penalty functions are widely used in the community of evolutionary optimization when coming to constraint handling. To avoid setting up penalty term, we introduce a new constraint handling method, in which a reference point selection mechanism and a population ranking process based on the distances to the selected reference point are proposed. The performance of our method is evaluated on 24 benchmark instances. Experimental results show that our method is competitive when compared with the state-of-the-art approaches and has improved the solution and the optima value of instance g22.
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Li, J., Shen, A., Lu, G. (2015). Reference Point Based Constraint Handling Method for Evolutionary Algorithm. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_32
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DOI: https://doi.org/10.1007/978-3-319-20466-6_32
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