自适应中心引力优化算法

针对函数全局优化问题,提出了一种自适应中心引力算法,以平衡全局探测能力和局部搜索能力。首先定义粒子的适应值函数,然后根据与平均适应值的比较,更新粒子运动时间,并引进交叉操作更新当前粒子位置,从而提高算法的收敛速度。最后选择8个典型测试函数进行测试,并与中心引力优化算法和其他粒子群优化算法进行比较。结果表明,该算法得到的结果十分精确,鲁棒性强,优于其他算法。

Adaptive Central Force Optimization Algorithm

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.