Abstract
Nonlinear functions optimization is still a challenging problem of great importance. This paper proposes a novel optimization technique called Evolutionary Elementary Cooperative Strategy (EECS) that integrates ideas form interval division in an evolutionary scheme. We compare the performances of the proposed algorithm with the performances of three well established global optimization techniques namely Interval Branch and Bound with Local Sampling (IVL), Advanced Scatter Search (ASS) and Simplex Coding Genetic Algorithm (SCGA). We also present the results obtained by EECS for higher dimension functions. Empirical results for the functions considered reveal that the proposed method is promising.
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Grosan, C., Abraham, A., Chis, M., Chang, TG. (2006). Evolutionary Elementary Cooperative Strategy for Global Optimization. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_86
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DOI: https://doi.org/10.1007/11893011_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46542-3
Online ISBN: 978-3-540-46544-7
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