International Journal of Computational Intelligence Systems

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Volume 12, Issue 2, 2019, Pages 1121 - 1133

Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

Authors
Na Wang1, Qingzheng Xu1, *, Rong Fei2, Jungang Yang1, Lei Wang2
1College of Information and Communication, National University of Defense Technology, 8 Zhangba East Road, Xi'an 710106, P.R. China
2School of Computer Science and Engineering, Xi'an University of Technology, 5 Jinhua North Road, Xi'an 710048, P.R. China
*Corresponding author. Email: xuqingzheng@hotmail.com
Corresponding Author
Qingzheng Xu
Received 6 June 2019, Accepted 30 September 2019, Available Online 14 October 2019.
DOI
10.2991/ijcis.d.191004.001How to use a DOI?
Keywords
Multi-factorial evolutionary algorithm; Multi-population evolution model; Multi-task optimization; Knowledge transfer; Across-population
Abstract

Multi-task optimization algorithm is an emergent paradigm which solves multiple self-contained tasks simultaneously. It is thought that multi-factorial evolutionary algorithm (MFEA) can be seen as a novel multi-population algorithm, wherein each population is represented independently and evolved for the selected task only. However, the theoretical and experimental evidence to this conclusion is not very convincing and especially, the coincidence relation between MFEA and multi-population evolution model is ambiguous and inaccurate. This paper aims to make an in-depth analysis of this relationship, and to provide more theoretical and experimental evidence to support the idea. In this paper, we clarify several key issues unsettled to date, and design a novel across-population crossover approach to avoid population drift. Then MFEA and its variation are reviewed carefully in view of multi-population evolution model, and the coincidence relation between them are concluded. MFEA is completely recoded along with this idea and tested on 25 multi-task optimization problems. Experimental results illustrate its rationality and superiority. Furthermore, we analyze the contribution of each population to algorithm performance, which can help us design more efficient multi-population algorithm for multi-task optimization.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
1121 - 1133
Publication Date
2019/10/14
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.191004.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Na Wang
AU  - Qingzheng Xu
AU  - Rong Fei
AU  - Jungang Yang
AU  - Lei Wang
PY  - 2019
DA  - 2019/10/14
TI  - Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model
JO  - International Journal of Computational Intelligence Systems
SP  - 1121
EP  - 1133
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.191004.001
DO  - 10.2991/ijcis.d.191004.001
ID  - Wang2019
ER  -