Decomposition-based evolutionary algorithm for large scale constrained problems

Cooperative Coevolutionary algorithms (CC) have been successful in solving large scale optimization problems. The performance of CC can be improved by decreasing the number of interdependent variables among decomposed subproblems. This is achieved by first identifying dependent variables, and by then grouping them in common subproblems. This approach has potential because so far no grouping technique has been mainly developed for constrained problems. In this paper, a new variable interaction identification technique to identify the dependent variables in large scale constrained problems is proposed. The proposed technique is tested on both a new test suite of constrained problems with medium and high dimensions, which include overlapping subproblems and different levels of complexity and nonseparability and also the established DED problem. The experimental results have shown that the proposed technique contributes to the decomposition approach over a range of high dimensions, in comparison with other state-of-the art grouping techniques. It achieves better performance with higher feasibility ratios and less computational time.

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