Overlapped cooperative co-evolution for large scale optimization

Abstract

The cooperative co-evolution (CC) framework is one of the most efficient methods to solve large scale optimization problems. The traditional CC framework divides decision variables into several mutually-exclusive groups. In this paper, we propose the overlapped cooperative co-evolution (OCC) framework for large scale optimization problems. In OCC framework, the decision variables that have strong impacts on the optimization are overlapped by different groups. First, we devise the delta-disturbance strategy to detect the influential variables. Then the overlapped grouping strategy is proposed to overlap the influential variables. Finally, the OCC framework is proposed to allocate more computation resources to the influential decision variables. To compare the performance of CC and OCC, we combine two frameworks with the random grouping strategy and the differential grouping strategy, and the comparative experiments are conducted on the CEC2010 benchmark functions. The experimental results verify that the proposed OCC framework is promising through comparing with the CC framework.