Abstract: As the current standard platform for cloud resource management,Kubernetes generally adopts improved methods based on swarm intelligence optimization algorithms for pod scheduling due to various shortcomings of its default scheduling mechanism.Tuna swarm optimization(TSO) is selected as the basic algorithm in this paper.And according to the ergodicity,randomness and other characteristics of chaos,a chaotic mapping based population initialization scheme is proposed to address the common problems of swarm intelligence optimization algorithms,such as susceptibility to initial values and premature convergence during later iterations.Various chaotic maps,such as Tent,Logistic,and so on,which are commonly involved in current research,are selected to initialize the tuna swarm respectively to improve the diversity of the initial population.Numerical experiments are conducted to compare the experimental results of the improved tuna swarm optimization algorithms based on different chaotic maps.It proves that the population initialization scheme based on chaotic maps can effectively improve the convergence speed and calculation accuracy of the original TSO algorithm.

Key words: Tuna swarm optimization algorithm, Chaotic map, Swarm intelligence optimization algorithm, Benchmark functions, Kubernetes