Origin-Oriented Shuffled Frog Leaping Vehicle Routing Multiobjective Optimization Algorithm

Related Work in Field of Shuffled Frog Leaping Algorithm

Scholars have improved the defects of SFLA and applied it to different fields. In the research of Wang et al. (2021), it introduced the intermediate factor and acceleration factor based on the idea of dichotomy search in mathematics. Shen et al. (2021) proposed a personalized tourism route recommendation method based on an improved hybrid leapfrog algorithm. Al-Ghussain et al. (2020) used shuffled frog leaping and pattern search to model a photovoltaic system. Cai et al. (2021) proposed a modified shuffled frog leaping algorithm with a ternary quantum. You et al. (2020) used an improved hybrid leapfrog algorithm to optimize fault diagnosis in a support vector machine.

The above research adopted different strategies to improve the performance of SFLA. However, the above methods all have defects of themselves. In this article, in the view of a local mechanism in the area with the origin as the center and the individual step size as the radius, the definitions of the frog leaping radius and frog oriented radius are defined. The frog's current position inertia and jump step in the local search are discarded so that the frog individuals gather near the origin with the maximum probability and in the area circle with the frog leaping radius or frog oriented radius as the neighborhood.