Abstract:

An improvement strategy of adaptive parameter adjustment is proposed to improve the efficiency of the shuffled frog leaping algorithm (SFL) in solving high dimensional complex problems. First of all, the convergence feature of the SFL is analyzed based on the theory of geometrical sequence. Then, an improvement strategy of adaptive parameter adjustment based on proportional coefficient and fitness standard deviation is proposed to the update the formula. Finally, based on three groups of eight criteria functions, the performance of the modified SFL with basic SFL and four modified particle swarm optimization (PSO) is compared, and the results verify the highefficiency of the improvement strategy for various complex functions. Meanwhile, the performance of the modified SFL with basic SFL and wPSO on solving high dimension problems is compared, and the results verify the validity of the modified SFL.