An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications.

Deyu Tang,Jin Yang,Jie Zhao,Zhen Liu,Yongming Cai,Mario Versaci

doi:10.1155/2021/8928182

Abstract

Shuffled frog leaping algorithm, a novel heuristic method, is inspired by the foraging behavior of the frog population, which has been designed by the shuffled process and the PSO framework. To increase the convergence speed and effectiveness, the currently improved versions are focused on the local search ability in PSO framework, which limited the development of SFLA. Therefore, we first propose a new scheme based on evolutionary strategy, which is accomplished by quantum evolution and eigenvector evolution. In this scheme, the frog leaping rule based on quantum evolution is achieved by two potential wells with the historical information for the local search, and eigenvector evolution is achieved by the eigenvector evolutionary operator for the global search. To test the performance of the proposed approach, the basic benchmark suites, CEC2013 and CEC2014, and a parameter optimization problem of SVM are used to compare 15 well-known algorithms. Experimental results demonstrate that the performance of the proposed algorithm is better than that of the other heuristic algorithms.

Highlights

In these years, a large number of complex nonlinear optimization problems are solved using mathematical tools by mathematic models
We focus on the research about the advantages and disadvantages of the shuffled frog leaping algorithm (SFLA) [28] and the related technique in order to propose a new approach for the continuous optimization problems
We find that the shuffled frog leaping algorithm has been successfully applied to many combinatorial optimization problems, but it is not efficient for the continuous optimization problem due to the weakness of balance between the exploration and the exploitation, such as the weakness of the exploitation ability and the loss of the exploration operator

Summary

Introduction

A large number of complex nonlinear optimization problems are solved using mathematical tools by mathematic models. In SI algorithms, the population is a set of individuals (solutions) distributed in search space, which can work in cooperation by survival or competition mechanism to solve problems In these years, more SI algorithms have been proposed, such as spotted hyena optimizer (SHO) [1], forest optimization algorithm (FOA) [2], particle swarm optimization (PSO) [3], whale optimization algorithm (WOA) [4], artificial bee colony (ABC) algorithm [5], grey wolf optimizer (GWO) [6], grasshopper optimization algorithm (GOA) [7], teachinglearning-based optimization (TLBO) [8], invasive tumor growth optimization (ITGO) algorithm [9], artificial algae algorithm (AAA) [10], and Salp swarm algorithm [11]

Methods

Results

Conclusion