用于函数优化的最大引力优化算法

Maximal Gravitation Optimization Algorithm for Function Optimization

提出一种基于牛顿万有引力定理的函数优化方法──最大引力优化算法.该算法通过"引力分组"和"引力淘汰"过程更新搜索体.文中给出4个引理来描述算法的数学基础,同时也给出算法的收敛性证明.此外还对该算法进行改进.最后与粒子群算法、差分算法、郭涛算法进行比较,数值结果显示该算法在解决连续函数优化问题具有较高的性能.

A global function optimization algorithm based on Newtong law of universal gravitation is proposed, namely maximal gravitation optimization algorithm （MGOA）. The search agents are updated through the processes of gravitational clustering and gravitational elimination, which are two main strategies in MGOA. Four lemmas are provided to describe the mathematical foundation, and the convergence of MGOA is strictly proved. Furthermore, the proposed algorithm is improved. The experimental result shows MGOA has good performance in solving continuous function optimization problems, compared with some well-known heuristic search methods such as Particle Swarm Optimization, Differential Evolution, and Guo Tao algorithm.