Clustering-Based Monarch Butterfly Optimization for Constrained Optimization

Authors

Sibo Huang^1, , Han Cui², Xiaohui Wei², Zhaoquan Cai^{3, 4, *}

¹Network and Information Center, Huizhou University, Huizhou 516007, China

²School of Electronic Information and Electrical Engineering, Huizhou University, Huizhou 516007, China

³School of Computer Science and Engineering, Huizhou University, Huizhou 516007, China

⁴Shanwei Institute of Technology, Shanwei 516600, China

^*Corresponding author. Email: 13502279833@126.com

DOI

10.2991/ijcis.d.200821.001How to use a DOI?

Keywords

Global optimization problem; Monarch butterfly optimization; Clustering; Greedy strategy; Constrained optimization

Abstract

Monarch butterfly optimization (MBO) algorithm is a newly-developed metaheuristic approach that has shown striking performance on several benchmark problems. In order to enhance the performance of MBO, many scholars proposed various strategies for benchmark evaluation and practical applications. As an application of artificial intelligence (AI), machine learning (ML) developed fast and succeeded in dealing with so many complicated problems. However, up to now, ML did not use to improve the performance of MBO algorithm. In this paper, one of ML techniques, clustering, is introduced into the basic MBO algorithm, so an improved clustering-based MBO namely CBMBO is proposed. In CBMBO algorithm, the whole population is divided into two subpopulations according to k-means clustering. Also, only the individuals having better fitness can be passed to the next generation instead of accepting all the updated individuals used in the basic MBO algorithm. In order to improve the diversity of the population, few individuals having worse fitness are accepted as new individuals. In order to verify the performance of our proposed CBMBO algorithm, CBMBO is compared with six basic and four improved metaheuristic algorithms on twenty-eight CEC 2017 constrained problems with dimension of 30, 50, and 100, respectively. The experimental results suggest a significant addition to the portfolio of computational intelligence techniques.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

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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TY  - JOUR
AU  - Sibo Huang
AU  - Han Cui
AU  - Xiaohui Wei
AU  - Zhaoquan Cai
PY  - 2020
DA  - 2020/09/23
TI  - Clustering-Based Monarch Butterfly Optimization for Constrained Optimization
JO  - International Journal of Computational Intelligence Systems
SP  - 1369
EP  - 1392
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200821.001
DO  - 10.2991/ijcis.d.200821.001
ID  - Huang2020
ER  -