An Improved Butterfly Optimization Algorithm for Engineering Design Problems Using the Cross-Entropy Method

Li, Guocheng; Shuang, Fei; Pan, Zhao; Le, Chengyi

doi:10.3390/sym11081049

Cited by 61 publications

(26 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[44] improved BOA to be used in Capacitated Vehicle Routing Problem (CVRP), where they applied intra route operators of CVRP, including move operator, swap operator, and inversion operator as local search operators in BOA. [45] improved BOA by using a cross-entropy method and applied it to benchmark functions and engineering design problems. [46] improved BOA by using the inertia weight of the PSO algorithm and random population restart strategy for optimising some benchmark functions.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

View full text Add to dashboard Cite

Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimisation algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimisation algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimisation process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics. INDEX TERMS Butterfly optimisation algorithm, feature selection, local search algorithm based on mutation.

show abstract

Section: Related Workmentioning

confidence: 99%

Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

View full text Add to dashboard Cite

show abstract

“…At present, butterfly optimization algorithm (BOA), as a recently proposed meta‐heuristic approach, has been continuously improved and applied in related fields based on engineering problems. In (Li, Shuang, Zhao, & Le, 2019), Guocheng Li et al proposed an improved butterfly optimization algorithm for engineering design problems, in which new method achieves a proper balance between exploration and exploitation with the embedding of cross‐entropy. To determine the best set of weights and biases in artificial neural networks, Jalali et al (2019) proposed a new classification method with combination of artificial neural networks and BOA algorithm, algorithm is applied as a new training strategy by optimizing the parameters.…”

Section: Modified Butterfly Optimization Algorithmmentioning

confidence: 99%

A modified butterfly optimization algorithm: An adaptive algorithm for global optimization and the support vector machine

Jiang

et al. 2020

Expert Systems

View full text Add to dashboard Cite

A modified adaptive butterfly optimization algorithm is established with the aim of addressing the “early search blindness” and the relatively poor adaptability of the sensory modality. A normal‐distribution‐based model and a Weibull‐distribution‐based adaptive model of sensory modalities are respectively proposed for the global search process and iteration process. Among them, the Weibull‐distribution‐based adaptive model of sensory modalities is mainly manifested as the c value, that is, the adaptive change trend based on the Weibull model. The performance of the modified butterfly optimization algorithm is validated using a 14‐benchmark test function and compared with performances of some latest algorithms. The experimental results indicate that the modified algorithm performs competitively in terms of accuracy and stability. Following the experiment, the modified algorithm is further tested by running a support‐vector‐machine prediction model based on engineering data of a pipe belt conveyor's flat‐pipe/pipe‐flat transition segment. The results of the modified algorithm are then compared with test‐run outcomes of the back‐propagation prediction model and KCV‐SVM model. The results show that the prediction error is well within 10%, demonstrating the method's competence as a reliable reference for future designs of pipe belt conveyors.

show abstract

“…Also, in [ 34 ] Arora and Anand embedded learning automata in BOA. Li et al in [ 35 ] proposed an improved version of BOA using Cross-Entropy method to achieve a better balance between exploration and exploitation. Arora and Anand proposed a binary version of BOA and applied it to the Feature Selection (FS) problem [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

On the performance improvement of Butterfly Optimization approaches for global optimization and Feature Selection

Assiri

2021

PLoS ONE

View full text Add to dashboard Cite

Butterfly Optimization Algorithm (BOA) is a recent metaheuristics algorithm that mimics the behavior of butterflies in mating and foraging. In this paper, three improved versions of BOA have been developed to prevent the original algorithm from getting trapped in local optima and have a good balance between exploration and exploitation abilities. In the first version, Opposition-Based Strategy has been embedded in BOA while in the second Chaotic Local Search has been embedded. Both strategies: Opposition-based & Chaotic Local Search have been integrated to get the most optimal/near-optimal results. The proposed versions are compared against original Butterfly Optimization Algorithm (BOA), Grey Wolf Optimizer (GWO), Moth-flame Optimization (MFO), Particle warm Optimization (PSO), Sine Cosine Algorithm (SCA), and Whale Optimization Algorithm (WOA) using CEC 2014 benchmark functions and 4 different real-world engineering problems namely: welded beam engineering design, tension/compression spring, pressure vessel design, and Speed reducer design problem. Furthermore, the proposed approches have been applied to feature selection problem using 5 UCI datasets. The results show the superiority of the third version (CLSOBBOA) in achieving the best results in terms of speed and accuracy.

show abstract

An Improved Butterfly Optimization Algorithm for Engineering Design Problems Using the Cross-Entropy Method

Cited by 61 publications

References 50 publications

Dynamic Butterfly Optimization Algorithm for Feature Selection

Dynamic Butterfly Optimization Algorithm for Feature Selection

A modified butterfly optimization algorithm: An adaptive algorithm for global optimization and the support vector machine

On the performance improvement of Butterfly Optimization approaches for global optimization and Feature Selection

Contact Info

Product

Resources

About