An Improved Shuffled Frog Leaping Algorithm with Comprehensive Learning for Continuous Optimization

DOI

10.2991/iccsee.2013.192How to use a DOI?

Keywords

Evolutionary compution, Shuffled forg leaping algorithm, Comprehensive learning strategy, Particle swarm optimization, Continuous Optimization

Abstract

This paper presents a shuffled frog leaping algorithm (SFLA) with comprehensive learning strategy (SFLA-CL) for global optimization. This algorithm uses a novel learning strategy whereby all other frogs’ information of the memplex is used to update the worst frog’s position. The strategy enables the diversity of the memplex to be preserved to discourage premature convergence. SFLA-CL also introduces a new search learning coefficient into the formulation of the original SFLA to enhance the convergence performance of SFLA. SFLA-CL has been evaluated, in comparison with existing evolutionary algorithm, such as SFLA, particle swarm optimization (PSO) and fast evolutionary programming (FEP), on five mathematical benchmark functions. Experimental results demonstrate that the SFLA-CL performs much better than SFLA, PSO, and FEP in optimizing these benchmark functions, particularly, in terms of its convergence rates and robustness.

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/).

Download article (PDF)

TY  - CONF
AU  - Liping Xue
AU  - Yinglong Yao
AU  - Hong Zhou
AU  - Zhiqiang Wang
PY  - 2013/03
DA  - 2013/03
TI  - An Improved Shuffled Frog Leaping Algorithm with Comprehensive Learning for Continuous Optimization
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 758
EP  - 761
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.192
DO  - 10.2991/iccsee.2013.192
ID  - Xue2013/03
ER  -