Abstract
In this paper, a new hybrid Good Point set algorithm is applied to solve the constrained problems. PSO frame can make the resulting evolutionary algorithm more robust and statically sound, especially for global optimization. Good Points Set, a concept in number theory, can make the local search achieve the same sound results just as the state-of-the-art methods do, such as orthogonal method. But the precision of the algorithm is not confined by the dimension of the space. An integrated mechanism is used to enrich the exploration and exploitation abilities of the approach proposed. Experiment results on a set of benchmark problems show the efficiency of the algorithm.
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Liu, Y., Li, S. (2010). Hybrid Good Point Set Evolutionary Strategy for Constrained Optimization. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_5
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DOI: https://doi.org/10.1007/978-3-642-14831-6_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14830-9
Online ISBN: 978-3-642-14831-6
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