Salp Swarm Algorithm Based on Refracted Opposition-based Learning
Because the salp swarm algorithm(SSA)is easy to fall into local optimum,resulting in poor convergence ability of the algo-rithm,in order to improve the search performance of the algorithm,this paper proposes a salp swarm algorithm based on refracted op-position-based learning(rOSSA).Firstly,the search agent uses refracted opposition-based learning to obtain the opposite solution in the solution space,so as to obtain more choices and increase the possibility of the algorithm to find better solution.In addition,the probabilistic perturbation mechanism is introduced into the refracted opposition-based learning species,so that the search agent can jump out of the local optimum in the later iteration,so as to enhance the global search ability of the algorithm.Finally,rOSSA is com-pared with some mainstream algorithms through nine unimodal,multimodal,and composite test functions and an engineering calcula-tion problem,and the experimental results effectively demonstrate the effectiveness of the improved algorithm.
NETL
NSTL
万方数据
