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A New Multi-Objective Binary bat algorithm for Feature Selection in Intrusion Detection Systems

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第4-5期37卷

作者： Laamari, Mohamed Amine Kamel, Nadjet Univ Setif 1 Ferhat ABBAS Fac Sci Dept Comp Sci LRSD Setif Algeria

Monitoring network traffic and detecting security threats is a vital task in today's world, and intrusion detection systems (IDS) have become an essential tool for this purpose. However, IDSs have to analyze large volumes of data, which often contain irrelevant and redundant features. This makes the job of IDSs more challenging, as they must sift through all available features to identify attack patterns, leading to longer processing time and reduced detection accuracy. To address this, we propose a new wrapper approach for solving the feature selection (FS) problem. Our proposed approach uses a novel multi-objective binary bat algorithm (MBBA-FS) with a decision tree classifier. The MBBA-FS aims to produce a set of non-dominated solutions that minimize the number of features used while maintaining a high detection accuracy. Then, we use a frequency ranking method to identify a single subset of relevant features from the resulting set of non-dominated solutions. We tested the feasibility and performance of our approach against other leading FS methods using various datasets, including KDD CUP 1999, NLS-KDD, UNSW-NB15, and several synthetic benchmarks. The experimental results show that MBBA-FS outperforms existing FS approaches in terms of classification accuracy and number of selected features.

关键词： bat algorithm data mining feature selection intrusion detection system multi-objective optimization

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Fedbat: a self-adapting bat algorithm-based federated learning approach

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-28页

作者： Wang, Jie Sun, Chaochao Peng, Yuan Shanghai Univ Elect Power Coll Comp Sci & Technol Shanghai 201306 Peoples R China

Federated learning (FL) is an advanced distributed machine learning (ML) framework designed to address issues related to data silos and data privacy. In real-world applications, common problems like non-convex optimization and nonindependent and identically distributed (Non-IID) client data reduce training efficiency, cause local optima, and degrade performance. Therefore, we propose a FL scheme based on the bat algorithm (Fedbat), which leverages the echolocation mechanism of bats to effectively balance global and local search capabilities, enabling the algorithm to escape local optima with a certain probability. By combining global optimal model weight optimization with dynamically adjusted search strategies, Fedbat guides weaker client models toward the global optimum, thereby accelerating convergence. Additionally, Fedbat allows for adaptive parameter adjustments across various datasets. To mitigate client drift, we extend Fedbat with Jensen-Shannon (JS) divergence to quantify differences between local and global models. Clients decide whether to upload their local models based on this divergence, to enhance the global model's generalization and minimize communication overhead. Experimental results demonstrate that Fedbat converges 5 times faster and enhances test accuracy by more than 40%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} compared to FedAvg. The extended Fedbat effectively mitigates the decrease in the generalization performance of the global model and reduces communication costs by approximately 20%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}. Compar

关键词： Federated learning Non-IID data bat algorithm Model convergence Self-adapting

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Adaptive interacting multiple model particle filter based on a dual-pattern bat algorithm for maneuvering target tracking

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2025年第4期239卷 293-311页

作者： Tian, Mengchu Chen, Zhimin Fang, Yubin Nanjing Univ Sci & Technol Sch Intelligent Mfg 200 Xiaolingwei St Nanjing 210094 Peoples R China China Satellite Maritime Tracking & Controlling De Jiangyin Peoples R China

The interacting multiple model particle filter (IMM-PF) is a filtering method commonly used for nonlinear non-Gaussian radar systems. Its transfer probabilities are usually fixed and dependent on prior information. When tracking maneuvering targets, the IMM-PF may cause excessive tracking errors due to the model switching lag. In addition, the particle impoverishment caused by the resampling of the particle filter seriously affects the filtering accuracy. Therefore, the IMM-PF has difficulty meeting the accuracy and speed requirements of modern high-performance radar target tracking systems. To address these problems, an adaptive interacting multiple model particle filter based on the dual-pattern bat algorithm (ADIMM-DPBA-PF) is proposed for tracking maneuvering targets under nonlinear and non-Gaussian conditions. First, the adaptive model switching mechanism is established to adjust the transition probabilities using the model probability posterior information at consecutive times, improving the model switching efficiency. Second, a particle filter based on the dual-pattern bat algorithm (DPBA-PF) is proposed. The filter exploits global search and local search strategies to optimize the particles and intelligently move the particles to the high likelihood region. Finally, a particle filter based on the dual-pattern bat algorithm is used to form the adaptive interacting multiple model method. The experimental results show that the proposed algorithm has better comprehensive performance than the IMM-PF.

关键词： Interacting multiple model maneuvering target tracking particle filter bat algorithm filtering algorithm

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Quantum-computing-driven bat algorithm: Balancing exploration-exploitation with predictive mutation and frequency adjustment for optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 283卷

作者： Chen, Si Slowik, Adam Chen, Yao She, Yan-Hong He, Xing-Shi Xian Shiyou Univ Sch Sci Dept Appl Math Xian 710065 Shaanxi Peoples R China Koszalin Univ Technol Koszalin Poland Xijing Univ Sch Comp Sci Xian 710123 Shaanxi Peoples R China Xian Polytech Univ Coll Sci 19 Jinhua South Rd Xian 710048 Shaanxi Peoples R China

Quantum computing (QC) has recently gained significant traction across various fields, spurring the integration of swarm intelligence algorithms into the quantum realm to boost their search capabilities. The bat algorithm (BA), modeled on bats' echolocation-based prey-hunting behavior, is a swarm intelligence algorithm, but it often gets stuck in local minima under certain conditions. To solve the problems, this research puts forward a hybrid predictive mutation operation and adaptive frequency adjustment strategy from a QC perspective to optimize the BA for better performance in complex problems. The method evolves the BA system into a highly uncertain complex nonlinear system via the state superposition principle, adds an optimization factor to balance global exploration and local exploitation, builds a rigorous mathematical model based on QC, and conducts a convergence analysis proof. Three parameter-control-mode experiments are designed to explore key parameter impacts. Numerical experiments on classic benchmarks and the three-dimensional trajectory planning for UAVs show that the proposed APMQF-QBA method outperforms comparison algorithms. Overall, the new strategy effectively improves the BA, avoiding local extrema and accelerating convergence, offering a more efficient solution for relevant fields.

关键词： Swarm intelligence bat algorithm Quantum computing Optimization

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Integration of bat algorithm and salp swarm intelligence with stochastic difference variants for global optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第8期27卷 10777-10818页

作者： Li, Hongye Wang, Jianan Zhu, Yanjie Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Xian Key Lab Big Data & Intelligent Comp Xian 710121 Shaanxi Peoples R China

This paper introduces a novel hybrid optimization algorithm named bat-salp swarm algorithm (BASSA). BASSA integrates the local exploitation capability of bat algorithm (BA) and the global exploration capability of salp swarm algorithm (SSA). Firstly, by introducing the echolocation of BA, the follower updating strategy of SSA is improved. Secondly, the algorithm selects between BA and SSA based on specific conditions. Finally, individuals undergo random differential mutation to increase population diversity, thereby avoiding local optima. To verify the effectiveness of the algorithm, we carry out experiments BASSA on 23 benchmark functions with different dimensions and compare it with 7 optimization algorithms, including BA, SSA, and 7 enhanced versions of SSA. Simulation results indicate that BASSA outperforms standard BA, SSA, and other enhanced algorithms in terms of mean and standard deviation. This suggests a significant improvement in optimization performance, with higher solution accuracy and faster convergence speed. Additionally, through performance evaluation on three real engineering problems, the results indicate that BASSA possesses strong optimization capabilities.

关键词： bat algorithm Salp swarm algorithm Stochastic difference variants Hybrid algorithm

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Hybrid momentum accelerated bat algorithm with GWO based optimization approach for spam classification

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第9期83卷 26929-26969页

作者： Dhal, Pradip Azad, Chandrashekhar Natl Inst Technol ITER Dept Comp Sci & Engn Jamshedpur 831014 Jharkhand India Siksha O Anusandhan Deemed Be Univ Dept Comp Sci & Engn Bhubaneswar 751030 Odisha India

Spam emails have become more prevalent, necessitating the development of more effective and reliable anti-spam filters. Internet users face security threats, and youngsters are exposed to inappropriate content while receiving spam emails. The gigantic data flow between billions of people and the tremendous number of features (attributes) makes the task more tiresome and complex. Feature Selection (FS) technique is essential for overwhelming accuracy, time and spatial complexity when we have high dimensional data (i.e., the number of features is very large). Spam emails have been successfully filtered and detected using Machine Learning (ML) methods by various researchers nowadays. This work proposes a hybrid binary Metaheuristic algorithm (MA) based Feature Selection (FS) approach for classifying email spam. The proposed FS approach is based upon two MA, i.e., bat algorithm (BA) with Grey Wolf Optimization(GWO). A novel concept of bat momentum has been introduced here, replacing the previous bat velocity. Two quantity, i.e., velocity and momentum, has an entirely different effect on the particle (i.e. bats). But they always follow the exact directions for both of them. To provide the best possible set of features for the FS process, the proposed approach uses an amalgamation technique to reach both the global and local optimum solution. To get the global optimum solution, a new momentum-based equation has been added to the BA, substituting the velocity equation from the prior BA. The GWO property has been added to the momentum-based equation mentioned above to improve the FS process search capabilities. Here a novel concept convergence timer has been introduced, which can eliminate the convergence issue in the iterative algorithm if it arises. A novel GWO based levy flight update has been introduced here to produce the local optimum solution. We have evaluated our proposed method on two benchmark spam corpora (Spambase, SpamAssassin) having different significant pro

关键词： Spam detection Feature selection bat algorithm Grey wolf optimization

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A hybrid chaotic bat algorithm for optimal placement and sizing of dg units in radial distribution networks

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ENERGY REPORTS 2024年 12卷 1723-1741页

作者： Khenissi, Imene Guesmi, Tawfik Alshammari, Badr M. Alqunun, Khalid Almalaq, Abdulaziz Alturki, Mansoor Neji, Rafik Univ Sfax Natl Engn Sch Sfax LETI Lab Dept Elect Engn Sfax Tunisia Univ Hail Coll Engn Dept Elect Engn Hail 55211 Saudi Arabia

Appropriate location and sizing of distributed generation (DG) units in a radial distribution system (RDS) play a significant role in the improvement of its overall performance. Indeed, proper DG placement and sizing help in maintaining the balance between power supply and demand, decreasing energy losses and enhancing voltage profile. Within this context, this paper presents an effective approach for the determination of the optimal location and the appropriate capacity of a photovoltaic distributed generation (PVDG) unit in RDSs. In the suggested approach, the best position of the PVDG is selected using the loss sensitivity factor (LSF). Meanwhile, a new optimization technique incorporating chaos, bats' self-adaptive compensation, and Doppler effect into the original bat algorithm (BA) is developed to find the optimal size of the PVDG unit. The optimal PVDG size is optimally determined in such a way that total active power losses of the RDS is reduced and voltage profile is enhanced. The performance of the proposed optimization technique, symbolized by (CSA-DC)BA, is evaluated using various benchmark functions. Moreover, the applicability and effectiveness of the suggested approach are verified on the IEEE 33-bus and the IEEE 69-bus RDSs. Obtained results revealed that the proposed (CSA-DC)BA outperforms the comparison optimization techniques.

关键词： Distributed generation Radial distribution systems Loss sensitivity analysis Chaos theory bat algorithm Doppler effect

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bat algorithm for Attractor Reconstruction of Low-Dimensional Chaotic Maps from Time Series 48

Bat Algorithm for Attractor Reconstruction of Low-Dimensiona...

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48th Annual IEEE International Computers, Software, and Applications Conference (COMPSAC) - Digital Development for a Better Future

作者： Galvez, Akemi Perez-Carabaza, Sara Iglesias, Andres Univ Cantabria Dept Appl Math & Comp Sci Santander Spain

ISBN: (纸本)9798350376975;9798350376968

Reconstructing the attractors of unknown chaotic systems from time series data presents a formidable challenge with broad applications across various disciplines. In this paper, we propose a swarm intelligence approach to address this challenge, focusing specifically on low-dimensional chaotic maps. Our approach is based on the bat algorithm, a renowned bio-inspired optimization technique well-suited for continuous optimization tasks. We evaluate the effectiveness and validity of our proposed approach by applying it to two distinct examples of chaotic maps: the Burger map and the Duffing map. Through comprehensive experimentation, we showcase the satisfactory performance of our method in reconstructing attractors from time series data. Based on our empirical findings, we conclude that our approach holds significant promise for the reconstruction of attractors of low-dimensional chaotic maps using time series data.

关键词： Dynamical systems chaotic maps attractor reconstruction time series swarm intelligence bat algorithm

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Optimisation of multi-objective cloud manufacturing service selection based on dynamic adaptive bat algorithm

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INTERNATIONAL JOURNAL OF WEB AND GRID SERVICES 2025年第1期21卷 58-87页

作者： Dai, Jie Zhu, Ming Li, Jing Zhao, Lianjun Zheng, Xiubao Shandong Univ Technol Coll Comp Sci & Technol Zibo Peoples R China

With the new round of changes in global manufacturing, cloud manufacturing, a new intelligent manufacturing model that integrates information manufacturing, cloud computing, and other technologies, has been proposed. It aims to rationalise manufacturing resource utilisation and adapt to complex user requirements. However, current research on service composition in cloud manufacturing mode does not fully consider the evaluation indicators of service quality and users' actual needs and constraints. Therefore, this paper first studies multiple service quality optimisation objectives and constraint problems in practical applications and then proposes a dynamic adaptive bat algorithm based on Gaussian mutation. The algorithm achieves fast convergence and reduces the probability of falling into the local extremum by dynamic random adjustment strategy. The effectiveness of the proposed method is verified by comparative experiments. The experimental results show that the proposed method is superior to other algorithms' solution quality and comprehensive performance.

关键词： cloud manufacturing service composition multi-objective optimisation inertia weight adaptive learning Gaussian mutation bat algorithm

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bat algorithm for optimizing a working vacation queue with impatient clients and secondary service

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OPSEARCH 2024年 1-20页

作者： Jyothsna, K. Kumar, P. Vijaya Laxmi, P. Vijaya GSS GITAM Dept Math Visakhapatnam 530045 Andhra Pradesh India Andhra Univ Dept Appl Math Visakhapatnam Andhra Pradesh India

This article investigates a Markovian working vacations queue with impatient clients and optional service. An arriving client can choose either to enter or not to enter the queue with a certain probability. Due to impatience, he may renege after joining the queue as per Poisson distribution. Two different kinds of services are offered by the server, namely, mandatory service which is required by all the clients entering the system and optional service which is opted by only few clients with certain probability after mandatory service. Just at the moment the system gets depleted, the server temporarily exits from the system for a short span called working vacations. Rather than shutting down completely throughout the working vacation duration, service is delivered at a different rate. The times between successive arrivals, vacation durations, service durations during mandatory service, during optional service and during working vacations are exponentially distributed and independent. Probability generating functions are used to evaluate the model's steady-state probabilities. The optimum service rate during mandatory service is obtained using bat algorithm. A table and a few graphs provide the numerical interpretations of the model for the impact of various model parameters on the system characteristics.

关键词： Mandatory service Optional service Working vacations Balking Reneging bat algorithm

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