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Elite-based butterfly optimization algorithm and its application in speckle projection technique

APPLIED SOFT COMPUTING 2025年 169卷

作者： Zhong, Xuxu Liang, Binbin Civil Aviat Adm China Chengdu Airborne Equipment Ctr Airworthiness Certificat Ctr Chengdu 610000 Peoples R China Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Peoples R China

In recent years, the butterfly optimization algorithm (BOA) has attracted a lot of attention because of its precise balance mechanism between exploitation and exploration. However, due to the shortcomings such as insufficient accuracy and slow convergence speed, the practical application of BOA is limited to a certain extent. This paper has two main contributions: 1) An elite-based butterfly optimization algorithm (eBOA) is proposed, which introduces an elite subgroup and gives full play to the positive role of elite elements in evolution. eBOA uses the optimal butterfly and the random elite butterfly to intervene in the global and local search operators respectively, thereby guiding the population to evolve in a more potential direction. Then, eBOA superimposes an existing parameter as a weight on the basis vector of the global search operator to speed up the convergence. 2) A preprocessing method for speckle images and disparity data that can be used in deep learning is proposed, and a novel 3D speckle reconstruction method is generated by combining eBOA with a deep network. In terms of results, comparative experiments based on 20 benchmark functions and 7 advanced meta-heuristic algorithms show that eBOA has excellent optimization capability;The comparison experiments of 5 deep networks confirm that the new reconstruction method can significantly improve the accuracy of speckle reconstruction models, and once again verify the remarkable optimization performance of eBOA.

关键词： Meta-heuristic algorithm butterfly optimization algorithm Speckle 3D reconstruction

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butterfly optimization algorithm: a novel approach for global optimization

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SOFT COMPUTING 2019年第3期23卷 715-734页

作者： Arora, Sankalap Singh, Satvir IK GUJRAL Punjab Tech Univ Jalandhar Punjab India

Real-world problems are complex as they are multidimensional and multimodal in nature that encourages computer scientists to develop better and efficient problem-solving methods. Nature-inspired metaheuristics have shown better performances than that of traditional approaches. Till date, researchers have presented and experimented with various nature-inspired metaheuristic algorithms to handle various search problems. This paper introduces a new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems. The framework is mainly based on the foraging strategy of butterflies, which utilize their sense of smell to determine the location of nectar or mating partner. In this paper, the proposed algorithm is tested and validated on a set of 30 benchmark test functions and its performance is compared with other metaheuristic algorithms. BOA is also employed to solve three classical engineering problems (spring design, welded beam design, and gear train design). Results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.

关键词： butterfly optimization algorithm Global optimization Nature inspired Metaheuristic Benchmark test functions Engineering design problems

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butterfly optimization algorithm based maximum power point tracking of photovoltaic systems under partial shading condition

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2023年第3期45卷 8337-8355页

作者： Aygul, Kemal Cikan, Murat Demirdelen, Tugce Tumay, Mehmet Cukurova Univ Elect & Elect Engn Adana Turkey Cumhuriyet Univ Elect & Elect Engn Sivas Turkey Adana Alparslan Turkes Sci & Technol Univ Elect & Elect Engn Adana Turkey

Because of dust, trees, high buildings in the surrounding area, partial shading conditions (PSC) occur in photovoltaic (PV) systems. This condition affects the power output of the PV system. Under PSC there is a global maximum power point (GMPP) besides there are a few local maximum power points (LMPP). This condition makes the maximum power point tracking (MPPT) procedure a challenging task. In order to solve this issue, soft computing techniques such as gray wolf optimization (GWO), particle swarm optimization (PSO) and Gravitational Search algorithm (GSA) are implemented. However, the performance of MPP trackers still needs to be improved. The main contribution of this paper is improving the tracking speed by implementing BOA to the MPPT of the PV system under PSC. Thus, in real-time applications a promising alternative presented to the literature to improve the performance of the PV systems under variable PSC because of its fast tracking speed. PV system consists of PV array, boost converter and load are modeled and simulated in MATLAB/Simulink. BOA algorithm is implemented for three different insolation scenarios on the PV array. The results of the BOA algorithm verified by a comparative analysis with PSO-GSA and GWO algorithms. The results show that BOA can give high accuracy and better tracking speed than these algorithms in recent literature.

关键词： Partial shading photovoltaic solar energy butterfly optimization algorithm MPPT

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butterfly optimization algorithm for optimum shape design of automobile suspension components

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MATERIALS TESTING 2020年第4期62卷 365-370页

作者： Yildiz, Betul Sultan Yildiz, Ali Riza Albak, Emre Isa Abderazek, Hammoudi Sait, Sadiq M. Bureerat, Sujin Bursa Uludag Univ Dept Automot Engn Bursa Turkey Bursa Uludag Univ Automot Engn Dept Bursa Turkey DTA Engn Bursa Turkey KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand

This article presents an implementation of one of the latest optimization methods of obtaining light vehicle designs. First, the problem of coupling with a bolted rim is optimized using the butterfly optimization algorithm (BOA). Finally, the BOA is used to solve the shape optimization of a vehicle suspension arm. It is utilized from the Kriging metamodeling method to obtain equations of objective and constraint functions in shape optimization. At the end of the research effort in this paper, the weight reduction of the suspension arm by using the BOA is 32.9%. The results show the BOA's ability to design better optimum components in the automotive industry.

关键词： butterfly optimization algorithm coupling with bolted rim kriging suspension arm shape optimization

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butterfly optimization algorithm using Penalty-Reward Analysis for Secure Sensing

Butterfly Optimization Algorithm using Penalty-Reward Analys...

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IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

作者： Gul, Noor Ahmed, Saeed Kim, Su Min Kim, Junsu Univ Peshawar Dept Elect Peshawar Pakistan Mirpur Univ Sci & Technol Dept Elect Engn Mirpur Ajk Pakistan Tech Univ Korea Dept Elect Engn Siheung Si Gyeonggi Do South Korea

ISBN: (数字)9781665471213

ISBN: (纸本)9781665471213

Cognitive radio (CR) is the best candidate for the growing demands of wireless connectivity in the internet of things (IoT). In the CR network, sensing performance is enhanced through cooperative spectrum sensing (CSS). However, security issues in CSS are to be tackled for reliable sensing. The malicious users (MUs) threaten the spectrum sensing that forward false sensing information to the fusion center (FC). To this end, this paper proposes an optimum CSS employing a penalty-reward-based butterfly optimization algorithm (PRBOA). The optimum coefficient vector enables FC to degrade the sensing notifications of the different categories of MUs in contrast with the normal secondary users (SUs). The coefficient vector and threshold are employed in the soft decision fusion to produce the global decision at the FC. A novel penalty-reward scheme is introduced in this work that assigns a penalty to the reports of MUs and rewards the normal SUs. The PRBOA scheme proposed in this work shows improved sensing results.

关键词： Penalty-reward Internet of Things butterfly optimization algorithm malicious users genetic algorithm particle swarm optimization

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mLBOA:A Modified butterfly optimization algorithm with Lagrange Interpolation for Global optimization

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Journal of Bionic Engineering 2022年第4期19卷 1161-1176页

作者： Sushmita Sharma Sanjoy Chakraborty Apu Kumar Saha Sukanta Nama Saroj Kumar Sahoo Department of Mathematics National Institute of Technology AgartalaAgartalaTripura 799046India Department of Computer Science and Engineering Iswar Chandra Vidyasagar CollegeBeloniaTripura 799155India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartalaTripura 799046India Department of Applied Mathematics Maharaja Bir Bikram UniversityAgartalaTripura 799004India

Though the butterfly Bptimization algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain ***,a new variant of BOA,namely mLBOA,is proposed here to improve its *** proposed algorithm employs a self-adaptive parameter setting,Lagrange interpolation formula,and a new local search strategy embedded with Levy flight search to enhance its searching ability to make a better trade-off between exploration and ***,the fragrance generation scheme of BOA is modified,which leads for exploring the domain effectively for better *** evaluate the performance,it has been applied to solve the IEEE CEC 2017 benchmark *** results have been compared to that of six state-of-the-art algorithms and five BOA ***,various statistical tests,such as the Friedman rank test,Wilcoxon rank test,convergence analysis,and complexity analysis,have been conducted to justify the rank,significance,and complexity of the proposed ***,the mLBOA has been applied to solve three real-world engineering design *** all the analyses,it has been found that the proposed mLBOA is a competitive algorithm compared to other popular state-of-the-art algorithms and BOA variants.

关键词： butterfly optimization algorithm Lagrange interpolation Levy flight search IEEE CEC 2017 functions Engineering design problems

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Non-dominated Sorting Advanced butterfly optimization algorithm for Multi-objective Problems

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Journal of Bionic Engineering 2023年第2期20卷 819-843页

作者： Sushmita Sharma Nima Khodadadi Apu Kumar Saha Farhad Soleimanian Gharehchopogh Seyedali Mirjalili Department of Mathematics National Institute of Technology AgartalaTripura799046India Department of Civil and Environment Engineering Florida International University MiamiMiamiFL33199USA Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia***Iran Centre for Artificial Intelligence Research and Optimization Torrens UniversityFortitude ValleyQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea

This paper uses the butterfly optimization algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization *** is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective *** to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance *** benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA(MONSBOA),including unconstrained,constrained,and real-world design multiple-objective,highly nonlinear constraint *** performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance *** is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80%occasions in solving problems with a variety of linear,nonlinear,continuous,and discrete characteristics based on the Pareto front when compared *** all the analysis,it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence.

关键词： Multi-objective problems butterfly optimization algorithm Non-dominated sorting Crowding distance

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Maximum Power Point Tracking Using Modified butterfly optimization algorithm for Partial Shading, Uniform Shading, and Fast Varying Load Conditions

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2021年第5期36卷 5569-5581页

作者： Shams, Immad Mekhilef, Saad Tey, Kok Soon Univ Malaya Dept Elect Engn Power Elect & Renewable Energy Res Lab Kuala Lumpur 50603 Malaysia Swinburne Univ Technol Fac Sci Engn & Technol Sch Software & Elect Engn Hawthorn Vic 3122 Australia Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia

In this article, a new maximum power point tracking algorithm based on a modified butterfly optimization algorithm has been proposed. The proposed method is capable of differentiating between different partial shading patterns, uniform shading, solar intensity, and load variation conditions with fast convergence speed (CS). Only one dynamic variable is used as a tuning parameter reducing the complexity of the algorithm. The search space skipping method has been proposed to improve the CS. The proposed method is hybridized with a constant impedance method to improve the response time of the system for fast varying load variations. The proposed method has been validated experimentally on the SEPIC converter topology with a sampling time of 0.05 s. The experimental validation proved the average tracking time for different shading patterns is less than 1 s with steady-state efficiency of 99.85% on average. The CS for uniform shading conditions is improved by 47.20%. The response to load variation is also improved by 86.15% and becomes eligible to be utilized for fast varying load variations. Finally, the comparison table based on the MPPT rating has been presented to determine the effectiveness of the proposed method among other popular metaheuristic approaches used for MPPT.

关键词： optimization Maximum power point trackers Load management Tuning Standards Heuristic algorithms Convergence butterfly optimization algorithm global maximum power point maximum power point tracking (MPPT) metaheuristic algorithms

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Improving the Accuracy of Network Intrusion Detection System in Medical IoT Systems through butterfly optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期126卷 1999-2017页

作者： Li, Ya Ghoreishi, Seyed-mohsen Issakhov, Alibek Jinan Univ Affiliated Hosp 5 Dept Informat Heyuan Guangdong Peoples R China Tech & Vocat Univ Dept Elect & Comp Engn Sary *** Mazandaran Iran Al Farabi Kazakh Natl Univ Alma Ata Kazakhstan Kazakh British Tech Univ Alma Ata Kazakhstan

Network intrusion detection systems analyze traffic in a medical IoT system to detect abnormal behaviors. Machine learning and artificial intelligence (AI) algorithms are widely used in designing intrusion detection systems to prevent attacks on a medical IoT system. In this paper, an artificial neural network is employed to detect abnormal behavior in a medical IoT system. The accuracy of the detection depends heavily on the features that are fed into the artificial neural network. Selecting the important and discriminative features of network traffic is a crucial and challenging issue because it has a significant impact on the learning process. In the proposed method, the butterfly optimization algorithm which is a meta-heuristic optimization algorithm is employed to select the optimal features for the learning process in an artificial neural network. The results achieved, 93.27% accuracy, indicate the capability of the butterfly optimization algorithm to determine discriminative features of network traffic data. The proposed algorithm outperformed the decision tree, support vector machine, and ant colony optimization, which was proposed in previous researches for the same goal.

关键词： Medical IoT system Intrusion detection system Artificial intelligence butterfly optimization algorithm Meta-heuristic algorithm

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Speaker recognition using Improved butterfly optimization algorithm with hybrid Long Short Term Memory network

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第30期83卷 73817-73839页

作者： Gade, Venkata Subba Reddy Manickam, Sumathi Sathyabama Inst Sci & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India

Speaker recognition is extensively applied in several applications, namely identity verification, electronic voice eavesdropping, surveillance, voice recognition, etc. In an effective speaker recognition system, the extraction and selection of salient and discriminative features is an essential process for accurately identifying the speakers. Therefore, a novel hybrid framework is introduced in this research manuscript. Initially, the input data are acquired from the three-benchmark databases: THUYG-20 SRE corpus, Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), and LibriSpeech. Further, the emotional features are extracted by utilizing hybrid feature extraction techniques which are, amplitude, zero cross rate, energy, Root Mean Square (RMS), statistical moments, autocorrelation, and Mel-Frequency Cepstral Coefficients (MFCC). Then, the feature optimization is carried out using Improved butterfly optimization algorithm (IBOA) that decreases the computational time and complexity of the recognition model. At last, a hybrid classifier: Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) is implemented for speaker recognition, and its performance is analyzed in terms of F1-score, specificity, accuracy, Positive Predictive Value (PPV), and sensitivity. The empirical investigation demonstrated that the IBOA-based hybrid LSTM network achieved 92.65%, 96.97% and 96.98% of recognition accuracy on the LibriSpeech, RAVDESS and THUYG-20 SRE corpus databases. These results are more impressive than the comparative models, Deep Neural Network (DNN), random forest, K-Nearest Neighbor (KNN), LSTM, Multi class Support Vector Machine (MSVM), Deep Convolutional Recurrent Neural Network (DCRNN), Golden Ratio aided Neural Network (GRaNN), deep sequential LSTM, and Probabilistic Neural Network (PNN).

关键词： butterfly optimization algorithm Convolutional neural network Long short-term memory network Speaker recognition Speech signal

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