Chaos-enhanced synchronized bat optimizer

•A chaotic mechanism was introduced to enhance bat optimization algorithm.•An inertial weight was set for the velocity to improve the ability of global exploration.•Extensive benchmark problems were used to verify the proposed method. In this paper, a chaos-enhanced bat algorithm is proposed to tack...

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Published inApplied Mathematical Modelling Vol. 77; pp. 1201 - 1215
Main Authors Yu, Helong, Zhao, Nannan, Wang, Pengjun, Chen, Huiling, Li, Chengye
Format Journal Article
LanguageEnglish
Published New York Elsevier Inc 01.01.2020
Elsevier BV
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ISSN0307-904X
1088-8691
0307-904X
DOI10.1016/j.apm.2019.09.029

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Summary:•A chaotic mechanism was introduced to enhance bat optimization algorithm.•An inertial weight was set for the velocity to improve the ability of global exploration.•Extensive benchmark problems were used to verify the proposed method. In this paper, a chaos-enhanced bat algorithm is proposed to tackle the global optimization problems. Bat algorithm is a relatively new stochastic optimizer inspired by the echolocation behavior of bats in nature. Due to its effectiveness, it has been applied to many fields such as engineering design, feature selection, and machine learning. However, the classical approach is often prone to falling into local optima. This paper proposes an enhanced bat algorithm to alleviate this problem observed in the original algorithm. The proposed method controls the steps of chaotic mapping by a threshold and synchronizes the velocity of agents using a velocity inertia weight. These mechanisms are designed to boost the stability and convergence speed of the bat algorithm, instantly. Eighteen well-established and the state-of-the-art meta-heuristic approaches are considered to validate the effectiveness of the developed algorithm. Experimental results reveal that the proposed chaos-enhanced bat algorithm is not only superior to the well-established algorithms such as the original method but also the latest improved approaches. Also, the proposed method is successfully applied to I-beam design problems, welded beam design, and pressure vessel design. The results show that chaos-enhanced bat algorithm can deal with unconstrained and constrained feature spaces, effectively.
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ISSN:0307-904X
1088-8691
0307-904X
DOI:10.1016/j.apm.2019.09.029