Large Scale Global Optimization using Differential Evolution with self-adaptation and cooperative co-evolution

In this paper, an optimization algorithm is formulated and its performance assessment for large scale global optimization is presented. The proposed algorithm is named DEwSAcc and is based on Differential Evolution (DE) algorithm, which is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces. The original DE is extended by log-normal self-adaptation of its control parameters and combined with cooperative co-evolution as a dimension decomposition mechanism. Experimental results are given for seven high-dimensional test functions proposed for the Special Session on Large Scale Global Optimization at 2008 IEEE World Congress on Computational Intelligence.

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