Abstract
In this paper, through the analysis of the artificial intelligence algorithm, shuffled frog leaping algorithm is effectively improved, and the position of the frog is determined by the quantum rotation angle, so as to improve the performance of the algorithm. Compared with the artificial bee colony algorithm and the shuffled frog leaping algorithm, the improved algorithm has a significant improvement in the convergence speed of the algorithm and the ability to jump out of the local area.
Highlights
With the continuous progress of society, more and more nonlinear extremum problems need to be solved urgently
Compared with genetic algorithm and particle swarm optimization algorithm, under the continuous optimization model, the convergence speed and jumping out of the local optimal solution, the shuffled frog leaping algorithm are better than the genetic algorithm, close to the particle swarm optimization algorithm[2]
Shuffled frog leaping algorithm has the common problems of the bionic intelligent algorithm, which is mainly manifested in that the local search ability weakens gradually with the progress of the algorithm, the population diversity decreases rapidly, and the effect is not obvious in solving the high-dimensional continuous optimization problem
Summary
With the continuous progress of society, more and more nonlinear extremum problems need to be solved urgently. Some bionic cluster intelligent algorithms are widely used, such as genetic algorithm, particle swarm optimization algorithm(pso), shuffled frog leaping algorithm and so on. Shuffled frog leaping algorithm has the common problems of the bionic intelligent algorithm, which is mainly manifested in that the local search ability weakens gradually with the progress of the algorithm, the population diversity decreases rapidly, and the effect is not obvious in solving the high-dimensional continuous optimization problem. Many scholars have improved Shuffled Frog Leaping Algorithm in order to improve the performance of the algorithm, mainly in the aspects of parameter adjustment, population update mode and the combination of intelligent algorithms. This study is mainly about shuffled frog leaping algorithm based on quantum Rotation Angle
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