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

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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Hybrid Multi-Strategy Improved butterfly optimization algorithm

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APPLIED SCIENCES-BASEL 2024年第24期14卷 11547页

作者： Cao, Panpan Huang, Qingjiu Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China Kogakuin Univ Grad Sch Engn Control Syst Lab Tokyo 1638677 Japan

To address the issues of poor population diversity, low accuracy, and susceptibility to local optima in the butterfly optimization algorithm (BOA), an Improved butterfly optimization algorithm with multiple strategies (IBOA) is proposed. The algorithm employs SPM mapping and reverse learning methods to initialize the population, enhancing its diversity;utilizes L & eacute;vy flight and trigonometric search strategies to update individual positions during global and local search phases, respectively, expanding the search scope of the algorithm and preventing it from falling into local optima;and finally, it introduces a simulated annealing mechanism to accept worse solutions with a certain probability, enriching the diversity of solutions during the optimization process. Simulation experimental results comparing the IBOA with Particle Swarm optimization, BOA, and three other improved BOA algorithms on ten benchmark functions demonstrate that the IBOA has improved convergence speed and search accuracy.

关键词： butterfly optimization algorithm SPM mapping reverse learning L & eacute vy flight sine and cosine algorithm simulated annealing algorithm

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A novel economic dispatch in the stand-alone system using improved butterfly optimization algorithm

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ENERGY STRATEGY REVIEWS 2023年 49卷

作者： Alhasnawi, Bilal Naji Jasim, Basil H. Bures, Vladimir Sedhom, Bishoy E. Alhasnawi, Arshad Naji Abbassi, Rabeh Alsemawai, Majid Razaq Mohamed Siano, Pierluigi Guerrero, Josep M. Al Furat Al Awsat Tech Univ Al Samawah Tech Inst Dept Elect Tech Al Samawah 66001 Iraq Basrah Univ Elect Engn Dept Basrah 61001 Iraq Univ Hradec Kralove Fac Informat & Management Hradec Kralove 50003 Czech Republic Mansoura Univ Fac Engn Elect Engn Dept Mansoura 35516 Egypt Al Muthanna Univ Coll Educ Pure Sci Dept Dept Biol Samawah 66001 Iraq Univ Hail Coll Engn Dept Elect Engn Hail City 81451 Saudi Arabia Imam Jaafar Al Sadiq Univ Coll Informat Technol Dept Comp Tech Engn Al Muthanna 66002 Iraq Salerno Univ Management & Innovat Syst Dept I-84084 Salerno Italy Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa Aalborg Univ AAU Energy Dept Ctr Res Microgrids CROM DK-9220 Aalborg Denmark

Distributed renewable energy systems are now widely installed in many buildings, transforming the buildings into 'electricity prosumers'. Additionally, managing shared energy usage and trade in smart buildings continues to be a significant difficulty. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. This study provides a coordinated smart building energy-sharing concept for smart neighborhood buildings integrated with renewable energy sources and energy storage devices within the building itself. This neighborhood energy management model's primary objective is to reduce the total power cost of all customers of smart buildings in the neighborhood by increasing the use of locally produced renewable energy. In the first stage, a group of optimum consumption schedules for each HEMS is calculated by an Improved butterfly optimization algorithm (IBOA). A neighborhood energy management system (NEMS) is established in the second stage based on a consensus algorithm. A group of four smart buildings is used as a test system to evaluate the effectiveness of the suggested neighborhood smart building energy management model. These buildings have varying load profiles and levels of integration of renewable energy. In this paper, the proposed framework is evaluated by comparing it with the Grey Wolf optimization (GWO) algorithm and W/O scheduling cases. With applying GWO, the total electricity cost, peak load, PAR, and waiting time are improved with 3873.723 cents, 21.6005 (kW), 7.162225 (kW), and 87 s respectively for ToU pricing and 11217.57 (cents), 18.0425(kW), 5.984825 (kW), and 98 s respectively for CPP tariff. However, using the IBOA Improves the total electricity cost, peak load, PAR, and waiting time by 3850.61 (cents), 20.1245 (kW), 6.7922 (kW), and 53 s respectively, for ToU and 10595.8 (cents), 17.6765(kW), 5.83255(kW), and 74 s f

关键词： butterfly optimization algorithm Grey wolf algorithm Solar energy Wind turbine Demand shifting

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A velocity-based butterfly optimization algorithm for high-dimensional optimization and feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 201卷 1页

作者： Long, Wen Xu, Ming Jiao, Jianjun Wu, Tiebin Tang, Mingzhu Cai, Shaohong Guizhou Univ Finance & Econ Key Lab Big Data Stat Anal Guiyang 550025 Peoples R China 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 Sch Energy & Elect Engn Loudi 417000 Peoples R China Changsha Univ Sci & Technol Sch Energy Power & Engn Changsha 410114 Peoples R China

Throughout the last decade, high-dimensional function optimization problems have received substantial research interest in the field of intelligence computation. butterfly optimization algorithm is a new meta-heuristic technique that has been proven to be more competitive than other optimization methods on low- dimensional problems. The basic butterfly optimization algorithm and its variants are not used to solve high-dimensional problems. Therefore, this paper develops an improved version of butterfly optimization algorithm to deal with the high- dimensional optimization problems. Inspired by particle swarm optimization, a modified position updated equation is designed by introducing velocity item and memory item in the local search phase to guide the search for candidate individuals. Moreover, a novel refraction-based learning strategy is introduced into butterfly optimization algorithm to effectively enhance diversity and exploration. The effectiveness of the proposed algorithm is tested on 40 benchmark problems with 100, 1000, 10,000 dimensions. The statistical results show that our algorithm has better performance than the basic butterfly optimization algorithm, its variants, and other population-based approaches to deal with the high-dimensional optimization problems. Finally, the high-dimensional feature selection and fault identification of wind turbine problems are solved and the comparisons show that the proposed algorithm outperforms better than most methods in terms of classification accuracy and number of the optimal feature subset.

关键词： butterfly optimization algorithm High-dimensional optimization Refraction-learning Feature selection Particle swarm optimization Global optimization

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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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A hybrid feature selection model based on butterfly optimization algorithm: COVID-19 as a case study

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EXPERT SYSTEMS 2022年第3期39卷 e12786页

作者： EL-Hasnony, Ibrahim M. Elhoseny, Mohamed Tarek, Zahraa Mansoura Univ Fac Comp & Informat Mansoura Egypt Amer Univ Emirates Coll Comp Informat Technol Dubai U Arab Emirates

The need to evolve a novel feature selection (FS) approach was motivated by the persistence necessary for a robust FS system, the time-consuming exhaustive search in traditional methods, and the favourable swarming manner in various optimization techniques. Most of the datasets have a high dimension in many issues since all features are not crucial to the problem, which reduces the algorithm's accuracy and efficiency. This article presents a hybrid feature selection approach to solve the low precision and tardy convergence of the butterfly optimization algorithm (BOA). The proposed method is dependent on combining the algorithm of BOA and the particle swarm optimization (PSO) as a search methodology using a wrapper framework. BOA is started with a one-dimensional cubic map in the proposed approach, and a non-linear parameter control technique is also implemented. To boost the basic BOA for global optimization, PSO algorithm is mixed with the butterfly optimization algorithm (BOAPSO). A 25 dataset evaluates the proposed BOAPSO to determine its efficiency with three metrics: classification precision, the selected features, and the computational time. A COVID-19 dataset has been used to evaluate the proposed approach. Compared to the previous approaches, the findings show the supremacy of BOAPSO for enhancing performance precision and minimizing the number of chosen features. Concerning the accuracy, the experimental outcomes demonstrate that the proposed model converges rapidly and performs better than with the PSO, BOA, and GWO with improvement percentages: 91.07%, 87.2%, 87.8%, 87.3%, respectively. Moreover, the proposed model's average selected features are 5.7 compared to the PSO, BOA, and GWO, with average features 22.5, 18.05, and 23.1, respectively.

关键词： butterfly optimization algorithm COVID-19 data classification feature selection particle swarm optimization

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Optimal Reactive Power Dispatch problem using exchange market based butterfly optimization algorithm

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Dora, Bimal Kumar Rajan, Abhishek Mallick, Sourav Halder, Sudip Natl Inst Technol Sikkim Ravangla 737139 Sikkim India Visvesvaraya Natl Inst Technol Nagpur 440010 India

The butterfly optimization algorithm (BOA) is a swarm-based optimization technique which takes its inspiration from the butterflies' foraging activity. BOA has gained extensive popularity among the research community and is now utilized to tackle a variety of optimization challenges. However, literatures suggest that its exploration and exploitation are not properly balanced. To overcome this problem, BOA is fused with the crossover of Exchange Market algorithm (EMA) and with non-uniform mutation. In this paper, enhanced BOA (EBOA) is used for solving the Optimal Reactive Power Dispatch (ORPD) problems. ORPD is traditionally a power system optimization tool that regulates control variables such as Generator bus Voltage, tap settings of tap-changing transformers, and VAR output of compensating devices in order to reduce real power loss, improve voltage deviation, and increase voltage stability. The suggested technique is evaluated using the IEEE 30 bus standard test system, IEEE 118 bus standard test system and Indian 62 bus system. To determine the efficacy of the algorithm, the findings from BOA and EBOA are compared to those produced by other researchers and published in the literature. From the results it can be observed that the proposed algorithm is able to reduce the active power loss by 22%, voltage deviation by 91.6% and L-Index by 42.02 % from their corresponding initial value for IEEE 30 bus system. Similar results can be seen from other test system also. The proposed algorithm is also tested in solving benchmark problems. The simulated results confirm the efficiency and robustness of the algorithm for solving ORPD problems.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Optimal Power Flow Metaheuristic algorithm butterfly optimization algorithm Optimal Reactive Power Dispatch

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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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Wavelet neural networks based solutions for elliptic partial differential equations with improved butterfly optimization algorithm training

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APPLIED SOFT COMPUTING 2020年 95卷 106518-106518页

作者： Tan, Lee Sen Zainuddin, Zarita Ong, Pauline Univ Sains Malaysia Sch Math Sci Usm 11800 Penang Malaysia Univ Tun Hussein Onn Malaysia Fac Mech & Mfg Engn Batu Pahat 86400 Johor Malaysia

In this study, a machine learning approach based on the unsupervised version of wavelet neural networks (WNNs) is used to solve two-dimensional elliptic partial differential equations (PDEs). The design of the WNNs must be judiciously addressed, particularly, the adopted training algorithm, since it greatly influences the generalization performance and the convergence rate of WNNs. Although the gradient information of the commonly used gradient descent training algorithm in WNNs may direct the search to optimal weight solutions that minimize the error function, the learning process is slow due to the complex calculation of the partial derivatives. To date, on account of the derivative free characteristic and adaptability to respond to the complex dynamic changes of the interdependencies, numerous studies explored the potential benefit of integrating a meta-heuristic algorithm as the training algorithm of WNNs, where encouraging results are achieved. In this paper, an improved butterfly optimization algorithm (IBOA) is proposed and subsequently integrated into the training process of the WNNs. To evaluate the performance of the proposed IBOA training method, the obtained results are compared to the results of the momentum backpropagation (MBP), the particle swarm optimization (PSO) and the standard butterfly optimization algorithm (BOA) training methods. Statistical analyses of the results based on a sufficient number of independent runs validate the effectiveness of the proposed method in terms of accuracy, robustness and convergence. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Wavelet neural networks Partial differential equations Elliptic partial differential equations Improved butterfly optimization algorithm butterfly optimization algorithm

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Chaotic butterfly optimization algorithm based on Particle Swarm optimization

International Core Journal of Engineering

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International Core Journal of Engineering 2022年第3期8卷 420-432页

作者： Gang Yang

In this article, I proposes an improved particle swarm hybrid butterfly optimization algorithm (IPBOA).In order to solve the problem of low accuracy and slow convergence in the butterfly optimization algorithm (BOA), I designed two strategies to improve the basic butterfly optimization algorithm (BOA) algorithm. The improved particle swarm hybrid butterfly optimization algorithm (IPBOA) includes three main steps. First, I added the cubic chaotic map to initialize the butterfly population. This has increased the diversity of butterfly populations to a certain extent; Secondly, I adopt a non-linear parameter control strategy. This strategy can effectively balance the global search and local search capabilities of the algorithm to a certain extent; finally, in order to improve the basic butterfly optimization algorithm (BOA) for global optimization, I mixed the particle swarm optimization (PSO) algorithm with BOA. In order to verify the effectiveness of the proposed algorithm, I selected 10 test functions. I use these test functions for comparative experiments. Experimental comparison results show that, compared with famous group optimization algorithms such as PSO and BOA,the performance of the IPBOA algorithm I proposed has been further improved. It has fast convergence speed, higher optimization precision and stronger robustness in the optimization problem of benchmark test functions.

关键词： butterfly optimization algorithm Cubic Chaos Nonlinear Parameters Particle Swarm optimization algorithm Benchmark Test Function

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