A Memetic Cooperative Co-evolution Model for Large Scale Continuous Optimization

Cooperative co-evolution (CC) is a framework that can be used to ‘scale up’ EAs to solve high dimensional optimization problems. This approach employs a divide and conquer strategy, which decomposes a high dimensional problem into sub-components that are optimized separately. However, the traditional CC framework typically employs only one EA to solve all the sub-components, which may be ineffective. In this paper, we propose a new memetic cooperative co-evolution (MCC) framework which divides a high dimensional problem into several separable and non-separable sub-components based on the underlying structure of variable interactions. Then, different local search methods are employed to enhance the search of an EA to solve the separable and non-separable sub-components. The proposed MCC model was evaluated on two benchmark sets with 35 benchmark problems. The experimental results confirmed the effectiveness of our proposed model, when compared against two traditional CC algorithms and a state-of-the-art memetic algorith