Abstract:

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.

Key words: Fermat’s principle, intelligent optimization, light ray optimization algorithm; local convergence