Abstract
A multi-context mechanism is newly reported to solve large-scale optimization problems (separable and nonseparable) within a general cooperative co-evolution (CC) framework. The basic CC is widely used to decompose a large-scale problem into several less difficult subproblems. When any two subproblems have no interaction, for example, when the problem is separable, the basic CC is effective. However, in practical cases there exist intensive interactions between subproblems, then the basic CC fails to find the global optimum. In this paper, the main reason of such failures has been studied and summarized. A general CC is proposed to use multiple context variables to avoid trapping caused by interactions. For the 500-dimension Rosenbrock's function with the optimum 0, the best result reported in existing CC methods is at the 102 level, but the global optimum can be found in the proposed CC. On a comprehensive set of benchmark, the proposed CC performs significantly better than existing CC in terms of accuracy.
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