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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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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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Performance of Quasi Oppositional butterfly optimization algorithm for Cluster Head Selection in WSNs

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Procedia Computer Science 2020年 171卷 1953-1960页

作者： Nageswara Rao Malisetti Vinay Kumar Pamula Department of ECE MIC College of Technology Kanchikacherla A.P. India 521180 Department of ECE UCEK JNTUK Kakinada India 533003

Wireless sensor networks (WSNs) technology is attracting the most of researchers and industry due to its application in wide contexts that include the military, surveillance, monitoring of habitat, health care, public safety and environmental monitoring. The consumption of energy is a crucial challenge faced in the WSN research. Clustering is an efficient technique for limiting the energy consumption of WSNs. But considering the hierarchical and cluster-based WSNs, the cluster heads utilize a high-level of energy because of data aggregation from various sensor nodes and transmit the same to the sink node. So, proper cluster head (CH) selection is required for sensor nodes’ energy conservation to prolong the network’s lifespan of the WSNs. In this paper, a CH selection protocol is proposed that is based on the quasi oppositional butterfly optimization algorithm (QOBOA). The proposed method is compared with original butterfly optimization along with certain existing algorithms with respect to the networks’ lifetime and efficiency of energy. Simulation results show that the QOBOA based CH selection scheme outperforms the existing ones.

关键词： butterfly optimization algorithm Cluster head Data aggregation Network lifespan

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Evolving Artificial Neural Networks Using butterfly optimization algorithm for Data Classification 26th

Evolving Artificial Neural Networks Using Butterfly Optimiza...

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26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Jalali, Seyed Mohammad Jafar Ahmadian, Sajad Kebria, Parham M. Khosravi, Abbas Lim, Chee Peng Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat IISRI Geelong Vic Australia Kermanshah Univ Technol Kermanshah Iran

ISBN: (纸本)9783030367084;9783030367077

One of the most difficult challenges in machine learning is the training process of artificial neural networks, which is mainly concerned with determining the best set of weights and biases. Gradient descent techniques are known as the most popular training algorithms. However, they are susceptible to local optima and slow convergence in training. Therefore, several stochastic optimization algorithms have been proposed in the literature to alleviate the shortcomings of gradient descent approaches. The butterfly optimization algorithm (BOA) is a recently proposed meta-heuristic approach. Its inspiration is based on the food foraging behavior of butterflies in the nature. Moreover, it has been shown that BOA is effective in undertaking a wide range of optimization problems and attaining the global optima solutions. In this paper, a new classification method based on the combination of artificial neural networks and BOA algorithm is proposed. To this end, BOA is applied as a new training strategy by optimizing the weights and biases of artificial neural networks. This leads to improving the convergence speed and also reducing the risk of falling into local optima. The proposed classification method is compared with other state-of-the-art methods based on two well-known data sets and different evaluation measures. The experimental results ascertain the superiority of the proposed method in comparison with the other methods.

关键词： butterfly optimization algorithm Artificial neural network Classification Meta-heuristic

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A modified butterfly optimization algorithm for mechanical design optimization problems

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2018年第1期40卷 1-17页

作者： Arora, Sankalap Singh, Satvir Yetilmezsoy, Kaan DAV Univ Dept Comp Sci & Engn Jalandhar Punjab India IK Gujral Punjab Tech Univ Dept Elect & Commun Engn Jalandhar Punjab India Yildiz Tech Univ Fac Civil Engn Dept Environm Engn TR-34220 Istanbul Turkey

This paper presents a modified butterfly optimization algorithm (MBOA) for solving mechanical design optimization problems. The modification is focused on an additional intensive exploitation phase which provides more chance to solutions to improve itself. The performance of the proposed algorithm is validated on fifteen benchmark test functions and three engineering design problems which have different natures of objective functions, constraints and decision variables. The experimental results are analyzed in comparison with those reported in the literature. The results indicate that the MBOA provides very competitive results in comparison with other existing optimization algorithms.

关键词： butterfly optimization algorithm Intensive exploitation Benchmark functions Engineering design problems

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An improved butterfly optimization algorithm with chaos

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第1期32卷 1079-1088页

作者： Arora, Sankalap Singh, Satvir IK Gujral Punjab Tech Univ Kapurthala Punjab India Shaheed Bhagat Singh State Tech Campus Ferozepur Punjab India

butterfly optimization algorithm (BOA) is a new corner in the category of nature inspired metaheuristic algorithms, inspired from food foraging behavior of the butterflies. Similar to other metaheuristic algorithms, it encounters two probable problems;(1) entrapment in local optima and (2) slow convergence speed. Chaotic maps are one of the best methods to improve the performance of metaheuristic algorithms. In the present study, chaos is introduced into BOA which increases its performance in terms of both local optima avoidance and convergence speed. Ten chaotic maps are employed to enhance the performance of the BOA. The proposed chaotic BOAs are validated on unimodal and multimodal benchmark test functions as well as on engineering design problems. The results indicate that the chaotic maps are able to significantly boost the performance of BOA.

关键词： butterfly optimization algorithm chaotic map metaheuristic optimization

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Learning automata-based butterfly optimization algorithm for engineering design problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL MATERIALS SCIENCE AND ENGINEERING 2018年第4期7卷

作者： Arora, Sankalap Anand, Priyanka DAV Univ Dept Comp Sci & Engn Jalandhar Punjab India Lovely Profess Univ Dept Comp Sci & Engn Jalandhar Punjab India

butterfly optimization algorithm (BOA) is a novel meta-heuristic algorithm inspired by the food foraging behavior of the butterflies. The performance of BOA critically depends upon the probability parameter which decides whether the butterfly has to move towards the best butterfly of the population or perform a random search. Therefore, in order to increase the potential of the BOA, which focuses on exploration phase in the initial stages and on exploitation in the later stages of the optimization, learning automata have been embedded in BOA in which a learning automaton takes the role of configuring the behavior of a butterfly in order to create a proper balance between the process of global and local search. The introduction of learning automata accelerates the global convergence speed to the true global optimum while preserving the main feature of the basic BOA. In order to validate the effectiveness of the proposed algorithm, it is evaluated on 17 benchmark test functions and 3 classical engineering design problems with different characteristics, having real-world applications. The simulation results demonstrate that the introduction of learning automata in BOA has significantly boosted the performance of BOA in terms of achievement of true global optimum and avoidance of local optima entrapment.

关键词： butterfly optimization algorithm learning automata adaptive behavior engineering design problems

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A novel adaptive butterfly optimization algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL MATERIALS SCIENCE AND ENGINEERING 2018年第4期7卷

作者： Singh, Bhupinder Anand, Priyanka Lovely Profess Univ Dept Comp Sci & Engn Jalandhar Punjab India

butterfly optimization algorithm (BOA) is an interesting bio-inspired algorithm that uses a nature inspired simulation model, based on the food foraging behavior of butterflies. A common problem with BOA is that in early stages of simulation process, it may converge to sub-optimal solutions due to the loss of diversity in its population. The sensory modality is the critical parameter which is responsible for searching new solutions in the nearby regions. In this work, an adaptive butterfly optimization algorithm is proposed in which a novel phenomenon of changing the sensory modality of BOA is employed during the optimization process in order to achieve better results in comparison to traditional BOA. The proposed Adaptive butterfly optimization algorithm (ABOA) is tested against seventeen standard bench mark functions. Its performance is then compared against existing standard optimization algorithms, namely artificial bee colony, firefly algorithm and standard butterfly optimization algorithm. The results indicate that the proposed adaptive BOA with improved parameter calculation mechanism produces superior results in terms of convergence and achievement of the global optimal solution efficiently.

关键词： Nature-inspired algorithm butterfly optimization algorithm adaptive algorithm sensory modality BOA convergence

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NHBBWO: A novel hybrid butterfly-beluga whale optimization algorithm with the dynamic strategy for WSN coverage optimization

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2025年第2期18卷 1-25页

作者： Chen, Xinyi Zhang, Mengjian Yang, Ming Wang, Deguang Guizhou Univ Sch Elect Engn Guiyang 550025 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Wireless sensor networks (WSNs) play a crucial role in the development of emerging technologies, but they face significant challenges in practical applications, particularly concerning the coverage problem. Effective coverage of sensor nodes is essential for ensuring high-quality service in WSNs. However, existing swarm intelligence algorithms for coverage optimization in WSNs often exhibit poor performance and suffer from simplistic experimental setups and low coverage rates. To address these challenges, this study proposes the hybrid butterfly-beluga optimization algorithm with a dynamic quadratic parameter adaptation strategy (NHBBWO) for optimizing WSNs coverage. NHBBWO combines the strengths of beluga whale optimization (BWO), known for rapid convergence, and butterfly optimization (BOA), which excels in global search but struggles with slow convergence and local optima. By hybridizing BWO and BOA, the hybrid butterfly-beluga whale optimization (HBBWO) algorithm improves both convergence accuracy and speed, as well as the ability to avoid local optima. Additionally, a dynamic parameter adaptation strategy using a quadratic function further enhances the exploration capability of HBBWO. The performance of NHBBWO is rigorously evaluated through statistical analyses, including the Friedman rank test and Wilcoxon rank-sum test, applied to twenty-three benchmark functions. The results confirm the superiority of NHBBWO. Finally, simulations in 2D coverage scenarios demonstrate that NHBBWO significantly improves node coverage and reduces redundancy compared to four state-of-the-art swarm intelligence algorithms. This advancement addresses critical limitations in current methodologies and contributes to the broader field of WSN optimization by providing a robust solution to the coverage challenge.

关键词： Wireless sensor network Coverage optimization Hybrid algorithm Beluga whale optimization butterfly optimization algorithm Dynamic parameter adaptation strategy

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Optimized automated detection of diabetic retinopathy severity: integrating improved multithresholding tunicate swarm algorithm and improved hybrid butterfly optimization

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2024年第1期12卷 42页

作者： Bhimavarapu, Usharani Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vijayawada Andhra Prades India

Diabetic retinopathy, a complication of diabetes, damages the retina due to prolonged high blood sugar levels, leading to vision impairment and blindness. Early detection through regular eye exams and proper diabetes management are crucial in preventing vision loss. DR is categorized into five classes based on severity, ranging from no retinopathy to proliferative diabetic retinopathy. This study proposes an automated detection method using fundus images. Image segmentation divides fundus images into homogeneous regions, facilitating feature extraction. Feature selection aims to reduce computational costs and improve classification accuracy by selecting relevant features. The proposed algorithm integrates an Improved Tunicate Swarm algorithm (ITSA) with Renyi's entropy for enhanced adaptability in the initial and final stages. An Improved Hybrid butterfly optimization (IHBO) algorithm is also introduced for feature selection. The effectiveness of the proposed method is demonstrated using retinal fundus image datasets, achieving promising results in DR severity classification. For the IDRiD dataset, the proposed model achieves a segmentation Dice coefficient of 98.06% and classification accuracy of 98.21%. In contrast, the E-Optha dataset attains a segmentation Dice coefficient of 97.95% and classification accuracy of 99.96%. Experimental results indicate the algorithm's ability to accurately classify DR severity levels, highlighting its potential for early detection and prevention of diabetes-related blindness.

关键词： Tunicate Swarm algorithm Renyi's entropy butterfly optimization algorithm Dice coefficient

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