Paper Info
Title
A mnemonic shuffled frog leaping algorithm with cooperation and mutation
Abstract
Shuffled frog leaping algorithm (SFLA) has shown its good performance in many optimization problems. This paper proposes a Mnemonic Shuffled Frog Leaping Algorithm with Cooperation and Mutation (MSFLACM), which is inspired by the competition and cooperation methods of different evolutionary computing, such as PSO, GA, and etc. In the algorithm, shuffled frog leaping algorithm and improved local search strategy, cooperation and mutation to improve accuracy and that exhibits strong robustness and high accuracy for high-dimensional continuous function optimization. A modified shuffled frog leaping algorithm (MSFLA) is investigated that improves the leaping rule by combining velocity updating equation of PSO. To improve accuracy, if the worst position in the memeplex couldn’t get a better position in the local exploration procedure of the MSFLA, the paper introduces cooperation and mutation, which prevents local optimum and updates the worst position in the memeplex. By making comparative experiments on several widely used benchmark functions, analysis results show that the performances of that improved variant are more promising than the recently developed SFLA for searching optimum value of unimodal or multimodal continuous functions.
Year
DOI
Venue
2015
10.1007/s10489-014-0642-x
Applied Intelligence
Keywords
Field
DocType
Shuffled frog leaping algorithm,Mnemonic velocity updating,Cooperation and mutation
Continuous function,Mathematical optimization,Local optimum,Computer science,Evolutionary computation,Robustness (computer science),Artificial intelligence,Exploration procedure,Local search (optimization),Optimization problem,Shuffled frog leaping algorithm,Machine learning
Journal
Volume
Issue
ISSN
43
1
0924-669X
Citations 
PageRank 
References 
6
0.50
15
Authors
3
Name
Order
Citations
PageRank
Hong-Bo Wang1123.71
Ke-Peng Zhang260.50
Xuyan Tu35214.94