Adaptive Central Force Optimization with Variable Population Size

A improving central force optimization algorithm (CFO) is proposed in this paper. New algorithm can adjust the size of population dynamically. With population evolution, algorithm balance the exploration and exploitation effectively. Experiments on 4 test functions show that the new algorithm is able to find good optimal solutions efficiently. Compared with existing algorithms, new algorithm improves solution accuracy with less computational effort.