斐波那契树优化算法全局随机性概率收敛分析

A global randomness-based probability convergence analysis of Fibonacci tree optimization algorithm

为改进斐波那契树优化算法的收敛性能,提出斐波那契树末梢自适应半径参数,使得算法在最优解邻域附近收敛能力显著提高.基于斐波那契树结构的全局随机性对斐波那契树优化算法的收敛性进行分析和证明.通过测试函数的求解精度比较、独立重复求解的收敛达标率比较实验验证了斐波那契树优化算法的收敛性能.

A Fibonacci tree-end self-adaptive radius parameter is proposed to effectively enhance convergence of Fibonacci tree optimization（FTO） algorithm at neighborhood of optima. The convergence of FTO is analyzed and proved on the basis of global randomness of Fibonacci tree. By comparing both the precision in solving benchmark functions and the qualified rate of repeated and independent solutions, the convergence performance of FTO is also examined and confirmed.