Convergence and Parameters Analysis of Shuffled Frog Leaping Algorithm

DOI

10.2991/icaise.2013.17How to use a DOI?

Keywords

swarm intelligence;shuffled frog leaping algorithm, Markov chain, convergence, parameters analysis

Abstract

Markov chain is an effective tool for convergence analysis of intelligence optimization algorithms. This paper briefly studies the state space of the basic Shuffled Frog Leaping Algorithm (SFLA) and theoretically analyzes the convergence behavior of SFLA by using Markov chain. It is proved that the SFLA has global convergence. Besides, the impact of key parameters on algorithm performance is discussed through simulation experiments. It provides the theoretical foundation and basis for using the algorithm to solve practical optimization problem.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lianguo Wang
AU  - Yaxing Gong
PY  - 2013/08
DA  - 2013/08
TI  - Convergence and Parameters Analysis of Shuffled Frog Leaping Algorithm
BT  - Proceedings of the 2013 The International Conference on Artificial Intelligence and Software Engineering (ICAISE 2013)
PB  - Atlantis Press
SP  - 71
EP  - 76
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaise.2013.17
DO  - 10.2991/icaise.2013.17
ID  - Wang2013/08
ER  -