earticle

영어

Bat algorithm is an optimization method inspired by the echo-location bats to search in nature, hunt prey behavior, combining multi-agent system and evolution mechanism. To improve the search results of BA algorithm, this paper proposes a gauss perturbation bats optimization algorithm based on simulated annealing (SAGBA). Firstly, the bionic principle, optimization mechanism and characteristics of the bat algorithm are analyzed and the algorithm optimization process are defined; Then the idea of the simulated annealing is put into bat optimization algorithm, and Gaussian disturbance is carried out to some individuals using the bat algorithm and strengthen the ability of the bat algorithm jumping out of local optimal solution. Finally, conduct simulations are respectively compared in 20 typical benchmark test functions among bat optimization algorithm, simulates annealing particle swarm algorithm and SAGBA algorithm. The results show that SAGBA algorithm not only increases the global convergence, but convergence speed and accuracy are better than other two algorithms.