Abstract: To improve the searching performance of gravitation move algorithm, in accordance with problems of bad performance in search accuracy and slow convergence speed in the high dimensional space optimization, PGMA is proposed by introducing affinity to improve the algorithm convergence and search precision, and this improved Gravitational Move Algorithm（GMA） changes the particle’s gravitational force calculation formula. It includes the principles of gravitational move algorithm and the structure of the affinity, and the affinity for the appropriate transformation is added to the formula resultant force. Then the formula resultant force is modified. Thirteen benchmarks function are tested and show that new algorithm is better than GMA with both a steady convergence and a better accuracy of solution.

Key words: Gravitation Move Algorithm（GMA）, resultant force, affinity, benchmark function, adjustable parameter

摘要： 为提高引力移动算法搜索性能，针对引力移动算法解决一些高维空间优化问题时存在的收敛速度慢、搜索精度不高的问题，提出一种基于亲和度的改进引力移动算法PGMA。基于引力移动算法原理，通过构造一个基于亲和度概念的系数对种群个体受到的引力合力公式作适当的变换改造基本引力移动算法。改进后的算法对种群中个体的位置更新方向加以引导，来提高算法的搜索精度和算法搜索能力。用13个基准函数对改进算法进行试验验证改进算法在求解精度和稳定性上优于基本引力移动算法。

关键词: 引力移动算法, 合力, 亲和度, 基准函数, 可调参数