Evolutionary elementary cooperative strategy for global optimization

Authors:
Crina Grosan;Ajith Abraham;Monica Chis;Tae-Gyu Chang
Affiliations:
Department of Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania;School of Computer Science and Engineering Chung-Ang University, Seoul, Korea;Avram Iancu University, Cluj-Napoca, Romania;School of Computer Science and Engineering Chung-Ang University, Seoul, Korea
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2006

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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.