贪婪光线寻优算法的局部收敛性分析

Local Convergence Analysis of Greedy Light Ray Optimization Algorithm

针对新型智能优化算法光线寻优算法局部搜索能力弱和收敛性理论完善困难的问题,提出一种贪婪光线寻优算法,并通过理论推导证明了该算法的局部收敛性.数值实验结果表明,对于单极值非线性标准测试函数,与粒子群算法和模拟退火算法相比,贪婪光线寻优算法具有更高的收敛精度和稳定性.

Light ray optimization algorithm is a new intelligent optimization algorithm with the weak local optimization ability and the difficulty of perfection of convergence theory.To solve these problems,greedy light ray optimization algorithm was proposed.Local convergence of the proposed algorithm was proved via theoretical derivation.Numerical experimental results show that for single extremal nonlinear standard testing functions,greedy light ray optimization algorithm has a higher convergent accuracy and stability compared with particle swarm optimization and simulated annealing.