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A Hybrid Firefly algorithm with butterfly optimization algorithm and its Application

ENGINEERING LETTERS 2022年第2期30卷 453页

作者： Zhang, Jinqian Xie, Xuefeng Wang, Min Zhang, Mengjian Guizhou Univ Sch Elect Engn Guiyang 550025 Peoples R China Guizhou Aerosp Kaishan Petr Instruments Co Ltd Guiyang 550025 Peoples R China Guizhou Univ Coll Elect Engn Guiyang 550025 Peoples R China

butterfly optimization algorithm (BOA) is a new nature-inspired algorithm that imitates the food-searching and mating behavior of butterflies to solve the global optimization problem. In nature, butterflies not only determine the locate nectar or mates by smell, but also the visual function of butterflies cannot be ignored. We proposed a novel hybrid firefly algorithm (FA) with BOA, namely FA-BOA, in which we take the visual function of the similarity of fireflies and butterflies into consideration. To substantiate the optimization performance of the proposed algorithm, FA-BOA is tested on a set of eight benchmark functions. Besides, the proposed algorithm is used to solve two real-world engineering design problems (Three-bar truss design and Speed reducer design). Experimental results demonstrate that the proposed algorithm is effective and outpeforms other optimization algorithms in terms of convergence accuracy and stability.

关键词： butterfly optimization algorithm firefly algorithm high dimension speed reducer design three-bar truss design

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Breast cancer prediction using a hybrid method based on butterfly optimization algorithm and Ant Lion Optimizer

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COMPUTERS IN BIOLOGY AND MEDICINE 2021年 139卷 104968-104968页

作者： Thawkar, Shankar Sharma, Satish Khanna, Munish Singh, Law Kumar Hindustan Coll Sci & Technol Dept Informat Technol Mathura Uttar Pradesh India RTM Nagpur Univ Dept Elect & Comp Sci Nagpur Maharashtra India Hindustan Coll Sci & Technol Dept Comp Sci & Engn Mathura Uttar Pradesh India

The design and development of a computer-based system for breast cancer detection are largely reliant on feature selection techniques. These techniques are used to reduce the dimensionality of the feature space by removing irrelevant or redundant features from the original set. This article presents a hybrid feature selection method that is based on the butterfly optimization algorithm (BOA) and the Ant Lion optimizer (ALO) to form a hybrid BOAALO method. The optimal subset of features chosen by BOAALO is utilized to predict the benign or malignant status of breast tissue using three classifiers: artificial neural network, adaptive neuro-fuzzy inference system, and support vector machine. The goodness of the proposed method is tested using 651 mammogram images. The results show that BOAALO outperforms the original BOA and ALO in terms of accuracy, sensitivity, specificity, kappa value, type-I, and type-II error as well as the receiver operating characteristics curve. Additionally, the suggested method's robustness is assessed and compared to five well-known methods using a benchmark dataset. The experimental findings demonstrate that BOAALO achieves a high degree of accuracy with a minimum number of features. These results support the suggested method's applicability for breast cancer diagnosis.

关键词： butterfly optimization algorithm Ant lion optimizer Artificial neural network Adaptive neuro-fuzzy inference system Support vector machine Feature selection Breast cancer Mammography

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

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EXPERT SYSTEMS 2021年第3期38卷 e12642-e12642页

作者： Hu, Kun Jiang, Hao Ji, Chen-Guang Pan, Ze State Key Lab Min Response & Disaster Prevent & C Huainan 232001 Peoples R China Anhui Univ Sci & Technol Huainan Peoples R China

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.

关键词： adaptive butterfly optimization algorithm support vector machine transition segment of the tubular belt conveyor

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Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization

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AD HOC NETWORKS 2021年 110卷 102317-102317页

作者： Maheshwari, Prachi Sharma, Ajay K. Verma, Karan Natl Inst Technol Delhi India IKG Punjab Tech Univ Jalandhar Punjab India

Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed sensor nodes connected through the wireless medium to monitor and record the physical information from the environment. The nodes of WSN are battery powered, so after a certain period it loose entire energy. This energy constraint affects the lifetime of the network. The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. At present, clustering and routing algorithms are widely used in WSNs to enhance the network lifetime. In this study, the butterfly optimization algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The cluster head selection is optimized by the residual energy of the nodes, distance to the neighbors, distance to the base station, node degree and node centrality. The route between the cluster head and the base station is identified by using Ant Colony optimization (ACO), it selects the optimal route based on the distance, residual energy and node degree. The performance measures of this proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption and data packets received by the BS. The outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods FUCHAR, CRHS, BERA, CPSO, ALOC and FLION. For example, the alive nodes of the proposed methodology are 200 at 1500 iterations which is higher compared to the CRHS and BERA methods.

关键词： Ant colony optimization butterfly optimization algorithm Energy consumption Network lifetime Wireless sensor networks

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Pinhole-imaging-based learning butterfly optimization algorithm for global optimization and feature selection

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APPLIED SOFT COMPUTING 2021年 103卷 107146-107146页

作者： Long, Wen Jiao, Jianjun Liang, Ximing Wu, Tiebin Xu, Ming Cai, Shaohong Guizhou Univ Finance & Econ Guizhou Key Lab Econ Syst Simulat Guiyang 550025 Peoples R China Guizhou Univ Finance & Econ Sch Math & Stat Guiyang 550025 Peoples R China Beijing Univ Civil Engn & Architecture Sch Sci Beijing 100044 Peoples R China Hunan Univ Human Sci & Technol Sch Energy & Elect Engn Loudi 417000 Peoples R China

butterfly optimization algorithm (BOA), as a recently proposed meta-heuristic optimization technique, performs competitively in solving numerical optimization problems as well as real-world applications. However, BOA has low precision, slow convergence and may be prone to local optimum, when solving complex or high-dimensional optimization problems. To overcome these defects, a modified BOA (called PIL-BOA) with adaptive gbest-guided search strategy and pinhole-imaging-based learning is proposed. Firstly, a modified position updated equation by introducing the global best (gbest) solution and the inertia weight is designed to efficiently improve the exploitation capability and the solution precision. Secondly, a novel pinhole-imaging learning strategy based on the principle of optics is presented to effectively search the unknown regions and avoid premature convergence. 23 classical problems and 60 complex optimization tasks from CEC 2014 and CEC 2017 are used to further investigate the effectiveness of PIL-BOA. The comparison results demonstrate that PIL-BOA has better performance than most compared algorithms on benchmark test functions. Finally, PIL-BOA is applied to solve feature selection problems and fault diagnosis in real-world wind turbine. The results show that PIL-BOA is superior to other competitors in term of classification accuracy. (C) 2021 Elsevier B.V. All rights reserved.

关键词： butterfly optimization algorithm Pinhole-imaging-based learning Global optimization Feature selection Fault diagnosis of wind turbine

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Enhanced butterfly optimization algorithm with a New fuzzy Regulator Strategy and Virtual butterfly Concept

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KNOWLEDGE-BASED SYSTEMS 2021年 228卷 107291-107291页

作者： Mortazavi, Ali Moloodpoor, Mahsa Ege Univ Grad Sch Nat & Appl Sci Izmir Turkey Ege Univ Dept Mech Engn Izmir Turkey

butterfly optimization algorithm (BOA) is a recently developed metaheuristic search algorithm that mimics the food-search process of the butterflies in nature. The studies reveal that BOA shows significant performance on the non-constrained and unbiased mathematical functions, but in the cases of shifted and rotated functions and/or constrained optimization problems, its search capacity is considerably restricted. To address this shortcoming, the present study deals with developing a new fuzzy decision-making strategy and introducing a new auxiliary concept called "virtual butterfly'' to enhance the search capability of the standard BOA. The developed fuzzy strategy permanently monitors the optimization process and tries to adjust each butterfly's search behavior based on the governing conditions of the current problem. Also, the virtual butterfly concept involving the information of the whole colony tries to provide alternative promising search directions for the other butterflies. The new reinforced method is named Fuzzy butterfly optimization algorithm (FBOA). To evaluate the search performance of the proposed FBOA, it is tested on solving a suit of constrained and non-constrained optimization problems and the achieved outcomes are compared with those given by some other well-established techniques including its parent method (i.e. BOA). The results show that the implemented improvements significantly increase the search capability of the regular BOA, especially in the case of constrained engineering problems. (C) 2021 Elsevier B.V. All rights reserved.

关键词： butterfly optimization algorithm Fuzzy logic Engineering optimization problems

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Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm

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ENERGY 2021年 229卷 120750-120750页

作者： Long, Wen Wu, Tiebin Xu, Ming Tang, Mingzhu Cai, Shaohong Guizhou Univ Finance & Econ Key Lab Econ Syst Simulat Guiyang 550025 Peoples R China Guizhou Univ Finance & Econ Sch Math & Stat Guiyang 550025 Peoples R China Hunan Univ Humanities Sci & Technol Dept Energy & Elect Engn Loudi 417000 Peoples R China Changsha Univ Sci & Technol Sch Energy Power & Engn Changsha 410114 Peoples R China

Establishing accurate and reliable models based on the measured data for photo-voltaic (PV) modules are significant to design, control and evaluate the PV systems. Although many meta-heuristic algorithms have been proposed in the literature, achieving reliable, accurate and quick parameters identification for PV models is still a challenge. This paper develops a variant of butterfly optimization algorithm (called EABOA) to identify the unknown parameters of PV models. In EABOA, a new position search equation and good-point set are proposed to balance between exploration and exploitation. 12 classical benchmark test problems are firstly selected for verifying the effectiveness of EABOA, and the results indicate that EABOA provides better performance than other selected algorithms. Then, EABOA is applied to identify the unknown parameters of three benchmark test PV models, i.e., single diode (SD), double diode (DD) and PV module models. The comparison results with some other reported parameter identification methods from literature suggest that the proposed EABOA outperforms most approaches in terms of accuracy and reliability. The least SIAE value of EABOA is smaller than other compared algorithms about 56.6%, 5.84%, and 10.2% for SD, DD, and PV module models, respectively. Finally, EABOA is applied to solve parameter identification problem of practical module and obtains the satisfactory results. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： butterfly optimization algorithm Photovoltaic models Parameter identification Global optimization

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Test Case optimization using butterfly optimization algorithm 10

Test Case Optimization using Butterfly Optimization Algorith...

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10th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Verma, Abhishek Singh Choudhary, Ankur Tiwari, Shailesh Shards Univ SET Greater Noida India ABES Engn Coll Ghaziabad India

ISBN: (纸本)9781728127910

Software cannot be release until unless it attains significant degree of confidence on quality parameters. In order to maintain the software quality, testing plays an important role. But this is a costly affair as it consumes almost 50 percent of the overall software development cost. The increasing competitiveness and ever updating technological change as well as customer requirements make regression testing a most important activity. So, regression testing is conducted before every release of the software which becomes expensive. optimization of regression test suite is a way to reduce this higher cost. This paper proposes an efficient self adaptive butterfly optimization technique. The proposed approach is further utilized on regression test suite optimization problem to reduce the regression test suite size. Performance of proposed approach has been evaluated against Bat Search optimization based approaches using fault detection as performance measures. Different tests are performed to analyze and validate the results. These results demonstrate the dominance of the proposed approach over the compared ones.

关键词： Regression Testing Test case optimization Nature inspired Metaheuristics butterfly optimization algorithm Software artifact infrastructure repository

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A Binary butterfly optimization algorithm for the Multidimensional Knapsack Problem 6

A Binary Butterfly Optimization Algorithm for the Multidimen...

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6th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Shahbandegan, Amirmohammad Naderi, Madjid Shahid Beheshti Univ Elect Engn Dept Tehran Iran Iran Univ Sci & Technol Elect Engn Dept Tehran Iran

ISBN: (纸本)9781728186290

The Multidimensional knapsack problem (MKP) is a well-known optimization problem with which many real-world engineering problems can be modeled. Due to its NP-hard nature, exact methods of solving the MKP are limited to small-scale problems. Therefore, in the past decade, various metaheuristics have been developed to solve the MKP in a reasonable time. butterfly optimization algorithm (BOA) is a recently developed meta-heuristic that has shown good convergence ability as well as avoiding local optima stagnation. In this paper, a binary version of BOA (BBOA) is proposed to solve the 0-1 MKP. BOA is originally designed for a continuous search space therefore in this paper, we propose six binary versions of BOA using three S-shaped and three V-shaped transfer functions and determine the most effective version through experiments. The proposed BBOA also includes an initial population generator and a repair operator based on the pseudo-utility. To evaluate the proposed method, 11 medium-scale and large-scale benchmark problems are employed and BBOA is compared to other competitive algorithms.

关键词： Multidimensional Knapsack optimization butterfly optimization algorithm

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Dual-objective Design of Multilayer Radar Absorbing Composite Material Using butterfly optimization algorithm 25

Dual-objective Design of Multilayer Radar Absorbing Composit...

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IEEE 25th International Seminar / Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED)

作者： Toktas, Abduirahim Ustun, Deniz Univ Karamanoglu Mehmetbey Fac Engn Elect Elect Engn Dept Karaman Turkey Univ Tarsus Fac Engn Comp Engn Dept Tarsus Mersin Turkey

ISBN: (纸本)9781728196954

A Multilayer Radar Absorbing Material (MRAM) based on composite is designed using a dual-objective optimization method butterfly optimization algorithm (BOA). The two objective functions regard Total Reflection (TR) involving sub-reflection at the inner layers and Total Thickness (TT). In order to make the MRA material more applicable for real applications;first, Mean Oblique Incidence (MOI) of TR in the range of 0-60 degree with polarizations TE/TM and then the average of MOI-TR in the range of 2-18 GHz is taken for achieving MOI-TRavg. The design variables, i.e. the thickness and material specimens for each layer from a material database including 21 composite specimens is optimally determined for synchronously minimizing the two objectives. Thus, a Global optimum MRAM (GMRAM) is picked out within the distributed candidate optimal solution set through Pareto optimality. Therefore, a five-layer GMRAM effectively absorbing at 7-18 GHz is successfully determined with the objectives of -10.85 dB MOI-TRavg and 4.2493 mm TT by considering the trade-off between the two objectives. The GMRAM is compared with a respective design in the literature to demonstrate the effectiveness of proposed optimization method. The TT is reduced as 66% by keeping the TR performance almost the same thanks to the effective and versatile dual-objective optimization with BOA.

关键词： radar absorbing material (RAM) multilayer radar absorbing material multi-objective optimization butterfly optimization algorithm composite

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