Smoothing and auxiliary functions based cooperative coevolution for global optimization

In this paper, a novel evolutionary algorithm framework called smoothing and auxiliary functions based cooperative coevolution (Briefly, SACC) for large scale global optimization problems is proposed. In this new algorithm pattern, a smoothing function and an auxiliary function are well integrated with a cooperative coevolution algorithm. In this way, the performance of the cooperative coevolution algorithm may be improved. In SACC, the cooperative coevolution is responsible for the parallel searching in multiple areas simultaneously. Afterwards, an existing smoothing function is used to eliminate all the local optimal solutions no better than the best one obtained until now. Unfortunately, as the above takes place, the smoothing function will lose descent directions, which will weaken the local search. However, a proposed auxiliary function can overcome the drawback, which helps to find a better local optimal solution. A clever strategy on BFGS quasi-Newton method is designed to make the local search more efficient. The simulations on standard benchmark suite in CEC´2013 are made, and the results indicate the proposed algorithm SACC is effective and efficient.