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Hybrid Mutualism Mechanism-Inspired butterfly and Flower Pollination optimization algorithm for Lifetime Improving Energy-Efficient Cluster Head Selection in WSNs

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WIRELESS PERSONAL COMMUNICATIONS 2023年第3期128卷 1567-1601页

作者： Pratha, S. Jaya Asanambigai, V Mugunthan, S. R. Annamalai Univ Dept CSE Chidambaram 608002 Tamil Nadu India Dr NGP Inst Technol Dept CSE Coimbatore 641048 Tamil Nadu India

Rapid advancement of technologies in Wireless Sensor Networks is attracting maximized attention across the scientific community due to its suitability and diversified coverage in real life applications. WSNs due to their features of resource limitation and infrastructure-less deployment introduces the most challenging issues of network lifetime improvement, energy stability and reliable cluster head selection, which is still a herculean task. Clustering is an indispensable mechanism employed for selecting an optimal cluster head with the objective of extending network lifetime with energy stability that achieves efficient data transmission. Cluster head selection through meta-heuristic algorithms introduces the merits of simplicity, flexibility, derivation free and prevents local optima. In this paper, Hybrid Mutualism Mechanism-inspired butterfly and Flower Pollination optimization algorithm (HMMB-FPOA) is proposed for energy-efficient cluster head selection that attributes towards better energy stability and sustained network lifetime. Mutualism phase includes the symbiosis organisms search over flower pollination optimization algorithm for incorporating strong exploitation capability into butterfly optimization algorithm that prevents losses of premature convergence as it may lose its diversity. This integration of FPOA and BOA improves the overall exploration ability to the expected level, such that convergence speed of the algorithms is accelerated. It also balances the capabilities of exploitation and exploration by dynamically increasing the adaptive switching probability that results in prolonged network lifetime. The simulation results of HMMB-FPOA confirmed an enhanced performance of the network in terms of alive nodes, dead nodes, residual energy, overall throughput, and convergence rate on par with the existing competitive cluster head selection algorithms.

关键词： Mutualism mechanism Cluster head selection butterfly optimization algorithm Flower pollination optimization algorithm Symbiosis organisms search

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Chaos-Enhanced Adaptive Hybrid butterfly Particle Swarm optimization algorithm for Passive Target Localization

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SENSORS 2022年第15期22卷 5739-5739页

作者： Rosic, Maja Sedak, Milos Simic, Mirjana Pejovic, Predrag Univ Belgrade Fac Mech Engn Belgrade 11000 Serbia Univ Belgrade Sch Elect Engn Belgrade 11000 Serbia

This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator's objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer-Rao lower bound (CRLB).

关键词： localization time difference of arrival butterfly optimization algorithm hybrid optimization particle swarm optimization Cramer-Rao lower bound

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Early diagnosis of liver disease using improved binary butterfly optimization and machine learning algorithms

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第10期83卷 30867-30895页

作者： Shaban, Warda M. Nile Higher Inst Engn & Technol Mansoura Egypt

Liver disease in patients is on the rise due to environmental factors like toxic gas exposure, contaminated food, drug interactions, and excessive alcohol use. Therefore, diagnosing liver disease is crucial for saving lives and managing the condition effectively. In this paper, a new method called Liver Patients Detection Strategy (LPDS) is proposed for diagnosing liver disease in patients from laboratory data alone. The three main parts of LPDS are data preprocessing, feature selection, and detection. The data from the patient is processed, and any anomalies are removed during this stage. Then, during feature selection phase, the most helpful features are chosen. A novel method is proposed to choose the most relevant features during the feature selection stage. The formal name for this method is IB(2)OA, which stands for Improved Binary butterfly optimization algorithm. There are two steps to IB(2)OA, which are;Primary Selection (PS) step and Final Selection (FS) step. This paper presents two enhancements. The first is Information Gain (IG) approach, which is used for initial feature reduction. The second is implementing BOA's initialization with optimization Based on Opposition (OBO). Finally, five different classifiers, which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF) are used to identify patients with liver disease during the detection phase. Results from a battery of experiments show that the proposed IB(2)OA outperforms the state-of-the-art methods in terms of precision, accuracy, recall, and F-score. In addition, when compared to the state-of-the-art, the proposed model's average selected features score is 4.425. In addition, among all classifiers considered, KNN classifier achieved the highest classification accuracy on the test dataset.

关键词： Liver Disease Feature selection butterfly optimization algorithm Classification

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A hybrid identification method on butterfly optimization and differential evolution algorithm

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SMART STRUCTURES AND SYSTEMS 2020年第3期26卷 345-360页

作者： Zhou, Hongyuan Zhang, Guangcai Wang, Xiaojuan Ni, Pinghe Zhang, Jian Beijing Univ Technol Minist Educ Key Lab Urban Secur & Disaster Engn Beijing 100124 Peoples R China Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Jiangsu Peoples R China

Modem swarm intelligence heuristic search methods are widely applied in the field of structural health monitoring due to their advantages of excellent global search capacity, loose requirement of initial guess and ease of computational implementation etc. To this end, a hybrid strategy is proposed based on butterfly optimization algorithm (BOA) and differential evolution (DE) with purpose of effective combination of their merits. In the proposed identification strategy, two improvements including mutation and crossover operations of DE, and dynamic adaptive operators are introduced into original BOA to reduce the risk to be trapped in local optimum and increase global search capability. The performance of the proposed algorithm, hybrid butterfly optimization and differential evolution algorithm (HBODEA) is evaluated by two numerical examples of a simply supported beam and a 37-bar truss structure, as well as an experimental test of 8-story shear-type steel frame structure in the laboratory. Compared with BOA and DE, the numerical and experimental results show that the proposed HBODEA is more robust to detect the reduction of stiffness with limited sensors and contaminated measurements. In addition, the effect of search space, two dynamic operators, population size on identification accuracy and efficiency of the proposed identification strategy are further investigated.

关键词： butterfly optimization algorithm differential evolution hybrid algorithm structural damage detection parameter identification dynamic adaptive operator

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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic butterfly optimization with Deep Learning Model

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Computers, Materials & Continua 2023年第8期76卷 1995-2013页

作者： Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with *** tracking(ET)has become a useful method to detect *** vital aspect of moral erudition is the aptitude to have common visual *** eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early ***-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD *** operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with *** purpose of this research is to use deep learning to identify autistic disorders based on eye *** Chaotic butterfly optimization technique is used to identify this specific ***,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic butterfly optimization with Deep Learning(ETASD-CBODL)*** presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL *** accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested *** addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter ***,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of *** assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data *** experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better perfo

关键词： Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis

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Suspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms

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GEOCARTO INTERNATIONAL 2022年第4期37卷 961-977页

作者： Fadaee, Marzieh Mahdavi-Meymand, Amin Zounemat-Kermani, Mohammad Shahid Bahonar Univ Kerman Water Engn Dept Kerman Iran

The present study investigates the capability of two metaheuristic optimization approaches, namely the butterfly optimization algorithm (BOA) and the Genetic algorithm (GA), integrated with machine learning models in Suspended Sediment Load (SSL) prediction. The Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) are applied as the predictive data-driven models. Independent input variables, i.e., the water temperature (T), river discharge (Q), and specific conductance (SC) are used for the prediction ofSSLbased on several statistical indices. The results indicate that the performances of all studied models were close to one another;moreover, the metaheuristic algorithms were found to increase the accuracy of the ANFIS and ANN models for approximately 11.73 percent and 4.30 percent, respectively. In general, the BOA outperformed the GA in enhancing the optimization performance of the learning process in the applied machine learning models.

关键词： Data mining butterfly optimization algorithm neural computing suspended sediment load

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Effective privacy preserving model based on adversarial CNN with IBOA in the social IoT systems for CEC

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第1期38卷

作者： Shukla, Prashant Kumar Pandit, Shraddha V. Gandhi, Charu Alrizq, Mesfer Alghamdi, Abdullah Shukla, Piyush Kumar Venkatareddy, Prashanth Rizwan, Ali Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India PES Modern Coll Engn Dept Artificial Intelligence & Data Sci Pune Maharashtra India Jaypee Inst Informat Technol Dept CSE IT Noida Uttar Pradesh India Najran Univ Coll Comp Sci & Informat Syst Informat Syst Dept Najran Saudi Arabia Rajiv Gandhi Proudyogiki Vishwavidyalaya Univ Inst Technol Dept Comp Sci & Engn Technol Univ Madhya Pradesh Bhopal 462033 Madhya Pradesh India NITTE Meenakshi Inst Technol Dept Elect & Elect Bangalore Karnataka India King Abdulaziz Univ Fac Engn Dept Ind Engn Jeddah Saudi Arabia

In the present scenario, the social Internet of Things (IoT) is one of the emerging technologies in combination with collaborative edge computing (CEC). The CEC solves the issue of storage and computing with the help of deep learning models that make full usage of the edge-computing abilities. The robustness of the deep learning models is ineffective, and the edge devices in the CEC are threatened by the malicious attacks. Therefore, a new data protection framework is proposed in this manuscript to avoid the security crisis and privacy leakage of CEC. A new adversarial sample generation model is introduced in this manuscript on the basis of intensive butterfly optimization algorithm (IBOA) that effectively reduces the time complexity of the framework to linear. Here, IBOA is chosen because an extra intensive exploitation step is included that guides the proposed framework to converge quickly towards global optimum and to avoid local optima trap. Then, the adversarial training concept is incorporated with the convolutional neural network (CNN) model for sentence similarity analysis. On the other hand, the anti-dropout layers are combined with the adversarial CNN model to reduce the overfitting concern. The proposed framework, adversarial CNN with IBOA, obtained higher results with f1-score of 88.70%, accuracy of 89.40%, correct rate of 88.95%, recall of 89.48%, and precision of 89.41% on the Microsoft Research Paraphrase Corpus (MSRP) dataset. The obtained results are superiorly better than the conventional deep learning models. This research introduced an adversarial sample generation model using the proposed intensive butterfly optimization algorithm (IBOA) that effectively reduces the time complexity of the framework. The adversarial data samples are created by implementing IBOA and the implementation of IBOA results in effective generation text with less linear time ***

关键词： adversarial samples butterfly optimization algorithm collaborative edge computing convolutional neural network Internet of Things privacy leakage security crises

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Data fusion algorithm of wireless sensor network based on clustering and fuzzy logic

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TELECOMMUNICATION SYSTEMS 2024年第4期86卷 617-626页

作者： Yu, Xiuwu Peng, Wei Zhang, Ke Zhou, Zixiang Liu, Yong Univ South China Sch Resource Environm & Safety Engn Hengyang 421001 Peoples R China Shenzhen Univ Coll Phys & Optoelect Engn Shenzhen 518000 Peoples R China

In order to reduce network energy consumption and prolong the network lifetime in wireless sensor networks, a data fusion algorithm named CFLDF is proposed. Firstly, upon completion of the arrangement of network nodes, network clustering is achieved using fuzzy c-means optimized by the improved butterfly optimization algorithm, and a data fusion model is established on the clustering structure. Then, reliable data is sent to the cluster head by the nodes with the assistance of a fuzzy logic controller, and data fusion is performed by the cluster head using a fuzzy logic algorithm. Finally, cluster heads transmit the fused data to the base station. Finally, the fused data is transmitted to the base station by the cluster heads. Simulation experiments are conducted to evaluate the CFLDF algorithm against the LEACH, LEACH-C, and SEECP algorithms. The results demonstrate that network energy consumption is effectively reduced and the network lifetime is extended by the CFLDF algorithm.

关键词： Wireless sensor network Data fusion Fuzzy c-means butterfly optimization algorithm Fuzzy logic

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A Novel Hybrid algorithm Based on butterfly and Flower Pollination algorithms for Scheduling Independent Tasks on Cloud Computing

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 842-850页

作者： Shao, Huiying Hebei Vocat Univ Technol & Engn Xingtai 054000 Peoples R China

Could computing is an Internet-based computing paradigm where virtual servers or workstations are offered as platforms, software, infrastructure, and resources. Task scheduling is considered one of the major NP-hard problems in cloud environments, posing several challenges to efficient resource allocation. Many metaheuristic algorithms have been extensively employed to address these task-scheduling problems as discrete optimization problems and have given rise to some proposals. However, these algorithms have inherent limitations due to local optima and convergence to poor results. This paper suggests a hybrid strategy for organizing independent tasks in heterogeneous cloud resources by incorporating the butterfly optimization algorithm (BOA) and Flower Pollination algorithm (FPA). Although BOA suffers from local optima and loss of diversity, which may cause an early convergence of the swarm, our hybrid approach outperforms such weaknesses by exploiting a mutualism-based mechanism. Indeed, the proposed hybrid algorithm outperforms existing methods while considering different task quantities with better scalability. Experiments are conducted within the CloudSim simulation framework with many task instances. Statistical analysis is performed to test the significance of the obtained results, which confirms that the suggested algorithm is effective at solving cloud-based task scheduling issues. The study findings indicate that the hybrid metaheuristic algorithm could be a promising approach to improving resource utilization and optimizing cloud task scheduling.

关键词： Task scheduling cloud computing butterfly optimization algorithm flower pollination algorithm mutualism

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Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第26期83卷 67283-67301页

作者： Javidan, Seyed Mohamad Banakar, Ahmad Vakilian, Keyvan Asefpour Ampatzidis, Yiannis Rahnama, Kamran Tarbiat Modares Univ Dept Biosyst Engn Tehran Iran Gorgan Univ Agr Sci & Nat Resources Dept Biosyst Engn Gorgan Iran Univ Florida Southwest Florida Res & Educ Ctr Agr & Biol Engn Dept 2685 FL-29 Immokalee FL 34142 USA Gorgan Univ Agr Sci & Nat Resources Fac Plant Prod Dept Plant Protect Gorgan Iran

Accurate diagnosis of plant diseases by the assessment of pathogen presence to reduce disease-related production loss is one of the most fundamental issues for farmers and specialists. This will improve product quality, increase productivity, reduce the use of fungicides, and reduce the final cost of agricultural production. Today, new technologies such as image processing, artificial intelligence, and deep learning have provided reliable solutions in various fields of precision agriculture and smart farm management. In this research, microscopic image processing and machine learning have been used to identify the spores of four common tomato fungal diseases. A dataset including 100 microscopic images of spores for each disease was developed, followed by the extraction of the texture, color, and shape features from the images. The classification results using random forest revealed an accuracy higher than 98%. Besides, as a reliable feature selection algorithm, the butterfly optimization algorithm (BOA) was used to detect the effective image features to identify and classify diseases. This algorithm recognized image textural features as the most effective features in the diagnosis and classification of disease spores. Considering only the eight most effective features selected with BOA resulted in an accuracy of 95% in disease detection. To further investigate the performance of the proposed method, its accuracy was compared with the accuracies of convolutional neural networks and EfficientNet as two reliable deep learning algorithms. Not only the prediction accuracy of these methods was not favorable (65 and 83.55%, respectively), they were very time-consuming. According to the findings, the proposed framework has high reliability in disease diagnosis and can help in the management of tomato fungal diseases.

关键词： Artificial intelligence butterfly optimization algorithm Disease diagnosis Microscopic image processing Morphological features Tomato disease spores

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