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37th Annual ACM Symposium on Applied Computing

作者： Nam Ngoc Pham Tomas Bata Univ Zlin Fac Appl Informat Nam TG Masaryka 5555 Zlin Czech Republic

ISBN: (纸本)9781450387132

High average utility pattern (itemset) Mining (HAUIM) is a necessary research problem in the field of knowledge discovery and data mining. Several algorithms have been proposed to mine high average-utility itemsets (HAUIs). Nonetheless, the large search space leads to poor performance because of excessive execution time and memory usage. To handle this limitation, particle swarm optimization (PSO) is applied to mine HAUIs. In this paper, an effective Binary PSO-based algorithm namely HAUIM-BPSO is proposed to explore HAUI efficiently. In general, HAUIM-BPSO first sets the number of discovered potential high average-utility 1-itemsets (1-PHAUIs) as the size of a particle based on average utility upper bound (AUUB) property. The sigmoid function is also used in the updating process of the individual of the proposed HAUIM-BPSO algorithm. Substantial experiments conducted on publicly available datasets show that the proposed algorithm has better results than existing state-of-the-art algorithms in terms of runtime which can significantly reduce the combinational problem, memory usage, and convergence speed.

关键词： High average-utility itemset mining bio-inspired algorithm Particle swarm optimization

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Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 392卷 114616-114616页

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Gandomi, Amir H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo Australia

This paper presents a novel bio-inspired algorithm inspired by starlings' behaviors during their stunning murmuration named starling murmuration optimizer (SMO) to solve complex and engineering optimization problems as the most appropriate application of metaheuristic algorithms. The SMO introduces a dynamic multi-flock construction and three new search strategies, separating, diving, and whirling. The separating search strategy aims to enhance the population diversity and local optima avoidance using a new separating operator based on the quantum harmonic oscillator. The diving search strategy aims to explore the search space sufficiently by a new quantum random dive operator, whereas the whirling search strategy exploits the vicinity of promising regions using a new operator called cohesion force. The SMO strikes a balance between exploration and exploitation by selecting either a diving strategy or a whirling strategy based on the flocks' quality. The SMO was tested using various benchmark functions with dimensions 30, 50, 100. The experimental results prove that the SMO is more competitive than other state-of-the-art algorithms regarding solution quality and convergence rate. Then, the SMO is applied to solve several mechanical engineering problems in which results demonstrate that it can provide more accurate solutions. A statistical analysis shows that SMO is superior to the other contenders. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Optimization algorithm Metaheuristic algorithm bio-inspired algorithm Swarm intelligence algorithm Quantum computing Applied mechanics and engineering problems

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Mining high average utility pattern using bio-inspired algorithm: student research abstract 22

Mining high average utility pattern using bio-inspired algor...

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Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

作者： Nam Ngoc Pham Tomas Bata University in Zlín Zlín Czech Republic

ISBN: (纸本)9781450387132

关键词： high average-utility itemset mining bio-inspired algorithm particle swarm optimization

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Research Trends of Autonomous Control Network for Applying the bio-inspired algorithm

Research Trends of Autonomous Control Network for Applying t...

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作者： Jungha Jin Ye-Won Oh Keecheon Kim Dept.of Computer Science & Engineering Konkuk University Dept.of IT Convergence Information Security Konkuk University

A variety of life forms that exist in the natural world, have been created evolving an efficient action rules for their own survival and the species of prosperity. And the attention from a variety of survival principle of these life forms, called bio-inspired algorithm to be applied to create the algorithm so as to be able to be applied in an environment other than the natural world. Natural environment itself has been included in various uncertain change, by that effects of problems how to efficiently utilize the limited resources environment, these bio-inspired algorithm, can provide a rapid adaptability to the conversion of the application environment, resources stably can provide scalability adaptability in constrained environment, can provide numerous advantages in terms of interoperability. When such a bio-inspired algorithms try to apply in terms of the network, in the former case, indicates that it is possible to easily provide an autonomous network configuration, in the latter case, resources such as IoT environment it is possible to provide the interoperability of a constraint type of environment. Thus, it is studied to connect to the network this bio-inspired algorithm, recent field of networking research issues and act in complementary, can provide a synergistic effect. bio-inspired algorithm techniques that can be used by applying congestion phenomena and Synchronization of nature in a network environment, has a variety of existing, autonomous control at SDN(Software Defined Networking) through a research to take advantage of this or incorporated into the configuration of the network, it is possible to develop in a direction to provide a more efficient interoperability of the same resource constrained environments with IoT environment. For the implementation of these bio-inspired autonomous control network environment, it summarizes which established OpenFlow environment and newly emerged technology P4:programming protocol-independent packet processor,

关键词： bio-inspired algorithm Software Defined Networking OpenFlow P4: Programming Protocol-Independent Packet Processors

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Parameter optimization in wire electrical discharge machining using bio-inspired algorithms and response surface methodology

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第2期19卷 705-732页

作者： Mohanraj, T. Thenarasu, M. Ragaventhra, B. Shree Pavilan, P. Jaswant, S. Kumar, R. Sandeep Panchu, K. Padmanabhan Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Mech Engn Coimbatore India Anna Univ Coll Engn Guindy Dept Ind Engn Chennai India

Wire electrical discharge machining (WEDM) is a highly regarded machining approach for its exceptional accuracy and adaptability, there is a need to further enhance its performance by optimizing critical process parameters. The objective is to examine the optimized setting of WEDM parameters with a focus on improving key performance metrics such as material removal rate (MRR), surface roughness (SR), and taper angle (TA). The desired responses are greatly influenced by critical process parameters, including pulse ON Time (50-60 mu s), pulse OFF Time (25-34 mu s), gap voltage (25-250 V), peak current (1-6 A), and dielectric flow rate (1-3 LPM). This study investigates the use of novel bio-inspired optimization algorithms, namely the African vulture optimization algorithm (AVOA), Firefly algorithm (FA), and cuckoo search algorithm (CSA), for optimizing process parameters in WEDM. The results are compared with the RSM approach. It was found that a significant reduction in MRR of 57.97%, an increase in TA of 6.21%, followed by enhancement in the surface finish of 22.98% attained with AVOA. The optimal parameters obtained with AVOA are TON of 51 mu s, TOFF of 30 mu s, peak current of 1 A, gap voltage of 116 V, and dielectric flow rate of 3 LPM. The integration of the RSM with bio-inspired algorithms in the manufacturing domain can be implemented in the manufacturing industries.

关键词： WEDM parameters African vulture optimization algorithm Firefly algorithm Cuckoo search algorithm bio-inspired algorithm RSM

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bio-inspired algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks

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SENSORS 2025年第1期25卷 72-72页

作者： Agrawal, Juhi Arafat, Muhammad Yeasir Univ Petr & Energy Studies Sch Comp Sci Dehra Dun 248007 India Chosun Univ IT Res Inst Gwangju 61452 South Korea

The high mobility and dynamic nature of unmanned aerial vehicles (UAVs) pose significant challenges to clustering and routing in flying ad hoc networks (FANETs). Traditional methods often fail to achieve stable networks with efficient resource utilization and low latency. To address these issues, we propose a hybrid bio-inspired algorithm, HMAO, combining the mountain gazelle optimizer (MGO) and the aquila optimizer (AO). HMAO improves cluster stability and enhances data delivery reliability in FANETs. The algorithm uses MGO for efficient cluster head (CH) selection, considering UAV energy levels, mobility patterns, intra-cluster distance, and one-hop neighbor density, thereby reducing re-clustering frequency and ensuring coordinated operations. For cluster maintenance, a congestion-based approach redistributes UAVs in overloaded or imbalanced clusters. The AO-based routing algorithm ensures reliable data transmission from CHs to the base station by leveraging predictive mobility data, load balancing, fault tolerance, and global insights from ferry nodes. According to the simulations conducted on the network simulator (NS-3.35), the HMAO technique exhibits improved cluster stability, packet delivery ratio, low delay, overhead, and reduced energy consumption compared to the existing methods.

关键词： aquila optimizer bio-inspired algorithm clustering FANETs mountain gazelle optimizer routing unmanned aerial vehicles UAV networks

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A Hybrid biologically-inspired Optimization algorithm for Data Gathering in IoT Sensor Networks

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2025年第2期33卷 1-32页

作者： Kponhinto, Gerard Khemiri-Kallel, Sondes Ari, Ado Adamou Abba Gueroui, Abdelhak Mourad Thiare, Ousmane Hwang, Junseok Paris Saclay Univ UVSQ David Lab Versailles France Gaston Berger Univ LANI Lab St Louis Senegal Univ Maroua LaRI Lab Maroua Cameroon Univ Garoua Inst Fine Arts & Innovat CREATIVE Garoua Cameroon Seoul Natl Univ Global R&DB Ctr Seoul South Korea

The wireless sensor networks are autonomous Ad hoc Networks that form the backbone of IoT technology. Their vital role is to collect data from their physical environments and transfer these data to the Base Station (BS). This task implies high energy consumption when a suitable data routing policy is not applied. Then, the data collection method determines the efficiency of WSNs. Moreover, the IoT-based applications that require the mobility of the IoT sensor nodes introduce a new challenge regarding network connectivity. The IoT sensor node's mobility leads to regular changes in the network topology, resulting in high packet loss during communication. The management of network energy use in order to prolong the battery life of IoT sensor nodes is a crucial issue, as IoT sensor nodes possess limited battery capacity. This paper addresses these issues by proposing an efficient Hybrid biologically-inspired Optimization algorithm, named HBIP, for Data Gathering in IoT Sensor Networks. The flagship idea of the HBIP algorithm is to incorporate the swarming stage of the Bacterial Foraging Optimization (BFO) into the Artificial Bee Colony (ABC) Optimization's exploitation phase. Simulation results show that the HBIP protocol outperforms the LEACH protocol and ABC and BFO metaheuristics regarding network energy consumption, lifetime, and data packets received at the BS. In practice, we demonstrated that the HBIP protocol increases the data collection by 84.40%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$84.40\%$$\end{document} over LEACH, 19.43%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$19.43\%$$\end{document

关键词： Data Gathering IoT Mobile Wireless Sensors Networks (MWSN) bio-inspired algorithm

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Surrogate-Assisted Multi-Objective Snow Goose algorithm With Gaussian Process for Cold Chain Logistics

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IEEE ACCESS 2025年 13卷 67350-67365页

作者： Wu, Jian Yang, Zong-Juan Du, Zhi-Gang Tian, Ai-Qing Tianjin Vocat Inst Sch Econ & Management Tianjin 300410 Peoples R China China Huaneng Grp Co Ltd Beijing Tendering Branch Beijing 102209 Peoples R China Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Peoples R China

This study presents a novel optimization framework, the Gaussian Process-Based Surrogate-Assisted Multi-Objective Snow Goose algorithm (GP-SAMOSGA), aimed at optimizing cold chain logistics (CCL) by addressing dual objectives: minimizing transportation costs and maximizing user satisfaction. User satisfaction is quantified through two key dimensions-time window compliance, which reflects delivery punctuality, and product freshness preservation, which evaluates the maintenance of perishable goods' quality. The optimization is conducted within essential operational constraints, such as temperature regulation, vehicle loading capacities, and specified delivery time windows. To address the computational complexity and high evaluation costs of the objective functions, the GP-SAMOSGA integrates a Gaussian Process (GP) surrogate model with the Multi-Objective Snow Goose algorithm (MOSGA). The GP surrogate model approximates the expensive objective functions, reducing the number of true function evaluations. Meanwhile, the MOSGA utilizes the migration and foraging behaviors of snow geese to balance exploration and exploitation in the search space. This integration allows the GP-SAMOSGA framework to efficiently identify Pareto-optimal solutions and form a well-distributed Pareto front that captures the trade-offs between competing objectives. The performance of the GP-SAMOSGA framework is validated through experiments on benchmark problems and a real-world CCL case study. The results demonstrate the algorithm's high solution quality, computational efficiency, and scalability. Moreover, the GP-SAMOSGA outperforms traditional multi-objective optimization algorithms, making it a promising tool for complex logistics optimization tasks. This research contributes to the field by combining surrogate modeling with bio-inspired optimization, offering a robust and efficient approach for multi-objective CCL optimization.

关键词： Optimization Costs Transportation Snow Cold chain Gaussian processes Computational modeling Time factors Vehicle routing Refrigeration Cold chain logistics optimization multi-objective optimization Gaussian process surrogate model bio-inspired algorithm

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Securing smart agriculture networks using bio-inspired feature selection and transfer learning for effective image-based intrusion detection

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INTERNET OF THINGS 2025年 29卷

作者： Saadouni, Rafika Gherbi, Chirihane Aliouat, Zibouda Harbi, Yasmine Khacha, Amina Mabed, Hakim Ferhat Abbas Univ Set 1 Sci Fac Comp Sci Dept LRSD Lab Setif 19000 Algeria Univ Bourgogne Franche Comte DISC FEMTO ST Inst F-25200 Montbeliard France

The smart agricultural system integrates advanced technologies to optimize farming practices and increase productivity. It gathers images from sensors, drones, and other sources to create detailed maps of crop yield variability, soil composition, and vegetation indices. In the realm of network security, the rise of smart agricultural systems presents both challenges and opportunities. This study addresses the crucial need for a tailored Intrusion Detection System (IDS) for agricultural networks, focusing on image-based traffic. Employing advanced techniques such as transfer learning and bio-inspired algorithms, we propose a novel IDS architecture adept at handling the unique characteristics of image traffic. Our methodology includes imbalanced data handling, transformation, feature extraction utilizing the pre-trained VGG16 model, feature selection via the bio-inspired Binary Greylag Goose (BGGO) algorithm, and classification employing a Random Forest classifier. Evaluation on the new CICIoT2023 dataset showcases high accuracy, with 99.41% for multiclass and 99.83% for binary classifications with a reduced number of features from 25088 to 6327. These results underscore the significance of efficient IDS solutions for the evolving landscape of agricultural technologies, promising enhanced network security in smart agriculture environments.

关键词： IDS IoT Image-based traffic VGG16 BGGO bio-inspired algorithm RF CICIoT2023

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Quantum bee-inspired algorithm using quantum circuit and gradient descent optimizer on product recommendation

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EVOLUTIONARY INTELLIGENCE 2025年第2期18卷 1-21页

作者： Bhaskaran, P. Prasanna, S. Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 Tamilnadu India

Bee-inspired algorithms, designed to mimic bee foraging activity, are renowned for their effectiveness in addressing complex optimization problems. Classic bee-inspired algorithms face challenges when applied to large-scale datasets due to computing limitations. Integrating quantum circuits into these algorithms has the potential to address this issue by improving computational efficiency and accuracy. This work intends to measure the performance of a quantum bee-inspired algorithm using quantum circuits and a gradient descent optimizer (QBIAGDO). The model's performance is compared with three other classical bee-inspired algorithms, such as BIAHCO, BIASAO, and BIAGAO, across the datasets: Bigbasket, supermarket sales, and Nykaa product review. The models were assessed based on computational efficiency and prediction accuracy, using measures such as iteration time, mean absolute error (MAE), and root mean squared error (RMSE). QBIAGDO regularly outperformed the other models, finishing iterations substantially faster and generating lower MAE and RMSE values. For the Bigbasket dataset, QBIAGDO completed 50 iterations in 37.55 s, with an MAE of 0.86 and RMSE of 0.91, exhibiting greater performance. The QBIAGDO shows itself to be the most efficient and accurate model among those examined, providing significant improvements in computing speed and prediction accuracy. This study illustrates the potential of QBIAGDO for large-scale predictive analytics, proposing its broader application across many sectors.

关键词： Quantum circuit Quantum optimization bio-inspired algorithm Entanglement

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