Abstract: The adaptive central force optimization(ACFO) algorithm was proposed for the global optimization problems in order to balance the abilities of global detective and local search. hhe particles fitness functions was defined. hhe partides movement time was updated based on the fitness value compared with the average fitness value, and the current position was updated by the crossover operation. As a result, the algorithm convergence speed was improved. 8 classic benchmark functions were used to test it Simulation results show that, ACFO is accurate, has strong robustness, compared with several other particle swarm optimization algorithms and CFO algorithms.

Key words: Central force optimization algorithm, Particle swarm optimization algorithm, Adaptive, Global optimization