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Algorithms 2018, 11(5), 71; https://doi.org/10.3390/a11050071

Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population

1
Department of Information Science and Technology, Huizhou University, Huizhou 516007, China
2
Educational Technology Center, Huizhou University, Huizhou 516007, China
*
Author to whom correspondence should be addressed.
Received: 21 March 2018 / Revised: 27 April 2018 / Accepted: 27 April 2018 / Published: 14 May 2018
(This article belongs to the Special Issue Algorithms for Decision Making)
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Abstract

Inspired by the migration behavior of monarch butterflies in nature, Wang et al. proposed a novel, promising, intelligent swarm-based algorithm, monarch butterfly optimization (MBO), for tackling global optimization problems. In the basic MBO algorithm, the butterflies in land 1 (subpopulation 1) and land 2 (subpopulation 2) are calculated according to the parameter p, which is unchanged during the entire optimization process. In our present work, a self-adaptive strategy is introduced to dynamically adjust the butterflies in land 1 and 2. Accordingly, the population size in subpopulation 1 and 2 are dynamically changed as the algorithm evolves in a linear way. After introducing the concept of a self-adaptive strategy, an improved MBO algorithm, called monarch butterfly optimization with self-adaptive population (SPMBO), is put forward. In SPMBO, only generated individuals who are better than before can be accepted as new individuals for the next generations in the migration operation. Finally, the proposed SPMBO algorithm is benchmarked by thirteen standard test functions with dimensions of 30 and 60. The experimental results indicate that the search ability of the proposed SPMBO approach significantly outperforms the basic MBO algorithm on most test functions. This also implies the self-adaptive strategy is an effective way to improve the performance of the basic MBO algorithm. View Full-Text
Keywords: monarch butterfly optimization; migration operator; butterfly adjusting operator; greedy strategy; benchmark problems monarch butterfly optimization; migration operator; butterfly adjusting operator; greedy strategy; benchmark problems
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Hu, H.; Cai, Z.; Hu, S.; Cai, Y.; Chen, J.; Huang, S. Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population. Algorithms 2018, 11, 71.

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