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A chaotic adaptive butterfly optimization algorithm

EVOLUTIONARY INTELLIGENCE 2024年第1期17卷 493-511页

作者： Peng, Maosong Wei, Xiuxi Huang, Huajuan Guangxi Minzu Univ Coll Elect Informat Nanning Peoples R China Guangxi Minzu Univ Coll Artificial Intelligence Nanning Peoples R China

butterfly optimization algorithm is a population intelligent optimization algorithm, which is easy to fall into local optimal and has low searching accuracy. In this paper, a chaotic adaptive butterfly optimization algorithm (CABOA) is proposed. Firstly, the Kent mapping is used to initialize the location of individual butterfly and increase the diversity of initial population. Secondly, adaptive switch probability is introduced in search phases to balance local search and global search phase. Then, the adaptive weight coefficient is introduced to adjust the search direction and the moving step of the individual butterfly to improve the convergence speed and accuracy of the algorithm. Finally, Cauchy variation is carried out to guide the advance direction of the population and avoid falling into local optimum. In this paper, the algorithm is used to obtain the optimal value of 12 benchmark test functions, and is applied to the design of welded beams. Experimental results show that the performance of the improved algorithm has significant advantages over other comparison algorithms: in the function optimization experiment, the average optimal value is improved by 99% compared with the original algorithm, and about 90% compared with other improved algorithms. Meanwhile, the algorithm proposed in this paper obtains the optimal value of all functions and has high efficiency. Compared with other existing optimization algorithms, CABOA has better convergence performance and certain competitiveness, and has certain advantages in the application field.

关键词： Adaptive switch probability Adaptive weight coefficient butterfly optimization algorithm Cauchy variation Kent mapping initialization

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Dynamic optimal network reconfiguration under photovoltaic generation and electric vehicle fleet load variability using self-adaptive butterfly optimization algorithm

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2021年第4期22卷 423-437页

作者： Pandraju, Thandava Krishna Sai Janamala, Varaprasad CHRIST Deemed Be Univ Sch Engn & Technol Dept Elect & Elect Engn Bangalore 560074 Karnataka India Dhanekula Inst Engn & Technol Dept Elect & Elect Engn Vijayawada 521139 Andhra Pradesh India

Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it's improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values.

关键词： butterfly optimization algorithm dynamic reconfiguration electrical distribution network electric vehicle fleet power loss renewable energy

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A novel approach of PSS optimal parameter tuning in a multi-area power system using hybrid butterfly optimization algorithm- particle swarm optimization

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2022年第5期13卷 2619-2628页

作者： Gude, Murali Krishna Salma, Umme GITAM Deemed Univ Dept Elect Elect & Commun Engn GIT Visakhapatnam 530045 Andhra Pradesh India

The small-signal stability is the cause of concern for modern-day power engineers. These mostly go undetected at the sender end but causes voltage fluctuations at the receiver end/ distributor end. The main reason behind this instability is the continuously varying operating point of the power system. Due to this, many types of controllers are being used whose nature are non-linear in nature. Thus, their effectiveness and swiftness to tackle such small-signal instability depend merely on their parameters. To fulfil this demand, various hybrid methods are being utilized. In this article, a novel optimization algorithm, Hybrid butterfly optimization algorithm-Particle Swarm optimization (HBOAPSO), has been used to tune the parameters of the Power System Stabilizer (PSS) employed in the Two Area Four Machines system. The obtained performance of HBOAPSO-PSS is then compared to other metaheuristic methods. The HBOAPSO-PSS has shown promising results. This can also be observed by the convergence graph of the HBOAPSO with respect to iteration. The inability of BOA to maintain a balance between local and global optima is removed by hybridizing it with PSO.

关键词： Power system stability butterfly optimization algorithm Multi area power system

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New robust hybrid Jarratt-butterfly optimization algorithm for nonlinear models

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第10期34卷 8207-8220页

作者： Sihwail, Rami Solaiman, Obadah Said Ariffin, Khairul Akram Zainol Amman Arab Univ Fac Comp Sci & Informat Dept Cyber Secur Amman 11953 Jordan Univ Kebangsaan Malaysia Fac Sci & Technol Dept Math Sci Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Cyber Secur Bangi 43600 Selangor Malaysia

The nonlinear system of equations (NSE) is the basis of many engineering and scientific models. However, these models must have accurate solutions to be successful. Several optimization algorithms, including butterfly optimization algorithm (BOA), can be applied to solving NSE problems. However, optimization algorithms lack accurate solutions due to their limitations, including trapping at local optima and diver-gence problems. In this work, a novel hybrid approach that is called Jarratt butterfly optimization algorithm (JBOA) is presented to solve NSE. The proposed JBOA is developed by combining Jarratt's iter-ative method and the BOA. Combining the two methods has enhanced BOA's search mechanism and con-vergence speed. It also overcomes Jarratt's limitations, including selecting initial values, trapping at local optima, and divergence problems. This results in solving NSE in more accurate solutions and fewer iterations. JBOA's efficiency was evaluated based on eight benchmark systems, two of which represent real-world applications. Further, JBOA was compared to several optimization algorithms, including the original BOA algorithm, Particle Swarm optimization (PSO), Harris Hawk optimization (HHO), Equilibrium optimization (EO), and Ant Lion Optimizer (ALO). Furthermore, the performance of JBOA and Jarratt's method has been compared. Finally, the experimental results support the superiority of JBOA as it significantly outperformed all other compared optimization algorithms in all benchmark systems in terms of best solutions, average fitness values, stability, and convergence speed.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Nonlinear systems Jarratt?s method Iterative methods butterfly optimization algorithm Hybrid systems

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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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An Anomaly-based Intrusion Detection System Using butterfly optimization algorithm 6

An Anomaly-based Intrusion Detection System Using Butterfly ...

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

作者： Mahboob, Amir Soltany Moghaddam, Mohammad Reza Ostadi Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

ISBN: (纸本)9781728186290

Intrusion detection system (IDS) is one of the most effective defensive systems which is on high demand due to the increasing number of devices connected to the internet these days. IDS monitors the network packets in order to detect malicious activities. These packets usually have a lot of features most of which are irrelevant and repetitive and can curtail the performance of the IDS. Therefore, it is important to use feature selection techniques to select an optimum subset of features. in this paper, a novel IDS is proposed which employs the butterfly optimization algorithm (BOA), a recent meta-heuristic, to perform feature selection. A multi-layer perceptron (MLP) classifier is used to evaluate the capability of the selected features to predict attacks. In order to improve the MLP classifier, in addition to the gradient descent (GD) training method, two other meta-heuristic methods, particle swarm optimization (PSO) and genetic algorithm (GA) have been used to optimize the classification structure. The NSL-KDD dataset is used in experiments to prove the efficiency of the proposed IDS in detecting four attack types namely: DoS, Probe, R2L and U2R. After repeated tests of the proposed method on four types of attack, the results are reported in terms of classification accuracy. A comparison of classical and intelligent methods in classifier training is made. Comparison with other similar works prove the superiority of the proposed method in reducing the number of features as well as increasing classification accuracy.

关键词： Intrusion detection system feature selection butterfly optimization algorithm multi-layer perceptron classifier

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Multiswarm Binary butterfly optimization algorithm for Solving the Multidimensional Knapsack Problem 29

Multiswarm Binary Butterfly Optimization Algorithm for Solvi...

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29th Iranian Conference on Electrical Engineering (ICEE)

作者： Shahbandegan, Shakiba Naderi, Madjid Iran Univ Sci & Technol Elect Engn Dept Tehran Iran

ISBN: (纸本)9781665433655

The multidimensional knapsack problem (MKP) is a well-known NP-hard combinatorial optimization problem which can be employed to model many practical engineering problems. Metaheuristic methods are proven efficient in solving NP-hard problems in a reasonable amount of time where exact methods face limitations. In the past decades, many heuristic methods have been developed to solve the MKP. butterfly optimization algorithm (BOA) is a recently developed metaheuristic method that has attracted the attention of various researchers due to its simplicity and potential as an optimization technique for global optimization problems in various applications. In this paper, the Multiswarm Binary BOA (MBBOA) is introduced to solve the 0-1 MKP. MBBOA employs a parallel search strategy to reach the optimum values in a reduced amount of time. To prove the efficiency of the proposed method, two experiments are conducted on 11 medium-scale and large-scale benchmark problems. Obtained results show that MBBOA is able to solve the MKP in a remarkably less amount of time compared with the sequential binary BOA algorithm.

关键词： optimization Multidimentioal knapsack problem butterfly optimization algorithm Swarm intelligence

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Circle Chaotic Search-Based butterfly optimization algorithm 15th

Circle Chaotic Search-Based Butterfly Optimization Algorithm

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Li, Wenting Yang, Jun Shao, Peng Jiangxi Agr Univ Sch Comp & Informat Engn Nanchang 330045 Jiangxi Peoples R China

ISBN: (纸本)9789819771806;9789819771813

Aiming at a series of problems of traditional butterfly optimization algorithm (BOA), such as lower solution accuracy, slower convergence speed, sensitivity to local optimum, and poorer stability, an improved butterfly optimization algorithm based on circle chaotic mapping and chaotic search strategy (CSBOA) is proposed. In CSBOA, the circle chaotic mapping is used for population initialization to obtain better initial solutions. Meanwhile, the chaotic search strategy is used to quadratically find the best value after global optimization to improve the algorithm's ability to jump out of local optima. 12 CEC2005 benchmark functions are selected for testing the performance of CSBOA, and the results of the simulation demonstrate that the CSBOA algorithm effectively accelerates the convergence speed, improves the convergence accuracy, and reduces the likelihood of falling into localized states.

关键词： butterfly optimization algorithm Chaotic Search algorithm Circle Chaotic Mapping

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