An effective cooperative coevolution framework integrating global and local search for large scale optimization problems

Cooperative Coevolution (CC) was introduced into evolutionary algorithms as a promising framework for tackling large scale optimization problems through a divide-and-conquer strategy. A number of decomposition methods to identify interacting variables have been proposed to construct subcomponents of a large scale problem, but if the variables are all non-separable, all the CC-based algorithms of decomposition will lose the functionality, therefore, classical CC-based algorithms are inefficient in processing non-separable problems that have many interacting variables. In this paper, a new CC framework which integrates global and local search algorithms is proposed for solving large scale optimization problems. In the stage of global cooperative coevolution, we introduce a new interacting variables grouping method named Sequential Sliding Window. When the performance of global search reaches a deviation tolerance or the variables are fully non-separable, we then use a more effective local search algorithm to subsequently search the solution space of the large scale optimization problem. The integration of global and local algorithms into CC framework can efficiently improve the capability in processing large scale non-separable problems. Experimental results on large scale optimization benchmarks show that the proposed framework is more effective than other existing CC frameworks.