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Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation
Joint Authors
Tian, Yun
Sun, Lin
Xu, Jiucheng
Chen, Suisui
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-10
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Abstract EN
Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms.
The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems.
However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems.
To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP).
Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population.
By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum.
Secondly, a new RLP is defined and introduced to improve the migration operator.
This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution.
A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency.
Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets.
Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.
American Psychological Association (APA)
Sun, Lin& Chen, Suisui& Xu, Jiucheng& Tian, Yun. 2019. Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation. Complexity،Vol. 2019, no. 2019, pp.1-20.
https://search.emarefa.net/detail/BIM-1131734
Modern Language Association (MLA)
Sun, Lin…[et al.]. Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation. Complexity No. 2019 (2019), pp.1-20.
https://search.emarefa.net/detail/BIM-1131734
American Medical Association (AMA)
Sun, Lin& Chen, Suisui& Xu, Jiucheng& Tian, Yun. Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation. Complexity. 2019. Vol. 2019, no. 2019, pp.1-20.
https://search.emarefa.net/detail/BIM-1131734
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131734