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bio-inspired algorithms for the characterization of excellent performance in handball players: A data-driven methodology

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 274卷

作者： Lopez-Gomez, Julio Alberto Romero, Francisco P. Angulo, Eusebio Univ Castilla La Mancha Dept Informat Syst & Technol Ciudad Real 13071 Spain Univ Castilla La Mancha Dept Math Ciudad Real 13071 Spain

bio-inspired algorithms have been successfully applied to solve complex optimization problems. They are also widely used to train and optimize machine learning and data-driven models, providing competitive results. This study presents a novel data-driven approach to identify and quantify the factors that characterize the excellent performance of handball players, depending on the specific position in which they play. This will give us the most important characteristics that differentiate the most excellent players in their positions. Based on bio-inspired algorithms, this research delves into the complex optimization problems inherent in sports analytics. The study utilizes data from the Women's European Handball Championship, employing seven distinct algorithms, of which six are different bio-inspired algorithms- including a hybrid bio-inspired algorithm- and one Consensus-Based Aggregation algorithm to analyze and assign weights to each player's actions during a match. This approach is further validated by comparing the findings against the top five players in each position as recognized by the European Handball Federation (EHF). Subsequently, the established model's robustness and applicability are tested using data from the Women's World Handball Championships.

关键词： Sports analytics Handball performance bio-inspired algorithms Data-driven modeling Optimization in sports

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bio-inspired algorithms for secure image steganography: enhancing data security and quality in data transmission

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第35期83卷 82247页

作者： Rezaei, Samira Javadpour, Amir Leiden Univ Leiden Inst Adv Comp Sci LIACS NL-2333 CA Leiden Netherlands Harbin Inst Technol Dept Comp Sci & Technol Cyberspace Secur Shenzhen Peoples R China Inst Politecn Viana Do Castelo ADiT Lab Elect & Telecommun Dept Viana Do Castelo Portugal

The proliferation of data sharing over the Internet has given rise to pressing concerns surrounding data security. Addressing these concerns, steganography emerges as a viable mechanism to safeguard data during transmission. It involves concealing messages within other media, such as images, exchanged over networks. In this research, we propose an innovative image steganography approach by harnessing the capabilities of bio-inspired algorithms. A central challenge in steganography revolves around the inherent pixel correlations within cover images, which may inadvertently leak sensitive information to potential intruders. To tackle this challenge head-on, we harness the potential of bio-inspired algorithms, which have exhibited promise in efficiently mitigating these vulnerabilities. This paper introduces a steganography strategy rooted in a fusion model that seamlessly integrates diverse bio-inspired algorithms. Our novel embedding approach ensures the production of robust and high-quality cover images and disrupts bit sequences effectively, thereby enhancing resistance against potential attacks. We meticulously evaluate the performance of our method using a comprehensive dataset encompassing grayscale and JPEG color images. Our particular emphasis on color images arises from their superior capacity to conceal a greater volume of information. The results vividly demonstrate our approach's effectiveness in achieving secure and efficient data concealment within images.

关键词： Genetic Algorithm bio-inspired algorithms Steganography Fusion Model

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bio-inspired algorithms for cybersecurity - a review of the state-of-the-art and challenges

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INTERNATIONAL JOURNAL OF bio-inspired COMPUTATION 2024年第1期23卷 1-15页

作者： Chui, Kwok Tai Liu, Ryan Wen Zhao, Mingbo Zhang, Xinyu Hong Kong Metropolitan Univ Sch Sci & Technol Dept Elect Engn & Comp Sci Hong Kong Peoples R China Wuhan Univ Technol Sch Nav Hubei Key Lab Inland Shipping Technol Wuhan 430063 Peoples R China Donghua Univ Sch Informat Sci & Technol Shanghai 200051 Peoples R China Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China

It is witnessed that the popularity of the research in cybersecurity using bio-inspired algorithms (a key subset of natural algorithms) is ever-growing. As an emergent research area, researchers have devoted efforts to applying and comparing various bio-inspired algorithms to cybersecurity applications. It is necessary to have a systematic review of bio-inspired algorithms for cybersecurity to fill the gap in the missing research study on this topic. The research contributions of this review article are four-fold. It first highlights the foundation of the baseline and latest development of 12 popular bio-inspired algorithms in three categories namely ecology-based, evolutionary-based and swarm intelligence-based algorithms. A systematic review is conducted to synthesise and compare the research methodologies, results and limitations. In-depth discussion will be made on the shortlisted and highly cited articles. The tips to select appropriate algorithm or the combination of multiple algorithms have been reported, along with the pros and cons on the design and formulations. Future research directions will be presented to meet the trends and unexplored research.

关键词： bio-inspired algorithms cybersecurity ecology-based algorithm machine learning evolutionary algorithms multi-objective optimisation swarm intelligence trade-off solution

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bio-inspired algorithms for feature engineering: analysis, applications and future research directions

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INFORMATION DISCOVERY AND DELIVERY 2025年第1期53卷 56-71页

作者： Rajput, Vaishali Mulay, Preeti Mahajan, Chandrashekhar Madhavrao Symbiosis Int Univ Dept Engn Pune India Vishwakarma Inst Technol Dept Engn Sci & Humanities Pune India

Purpose - Nature's evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains. Design/methodology/approach - bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of "bio-inspired optimization"-based computational models by referring to vast research literature published between year 2015 and 2022. Findings - The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The "National Natural Science Foundation" of China and the "Ministry of Electronics and Information Technology" of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research. Originality/value - The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

关键词： bio-inspired algorithms Evolutionary algorithms Speech emotion recognition Sentiment analysis Nature-inspired optimization algorithms Feature engineering bio-inspired computing bio-inspired optimization bio-inspired application Feature selection

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Improving Safety and Efficiency of Industrial Vehicles by bio-inspired algorithms

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EXPERT SYSTEMS 2025年第3期42卷

作者： Bayona, Eduardo Sierra-Garcia, J. Enrique Santos Penas, Matilde Univ Burgos Dept Digitalizat Burgos 09006 Spain Arainnov Michelin Aranda UBU MICHELIN Joint Res Unit Automat & Smart Ind Aranda De Duero 09400 Spain Univ Complutense Madrid Inst Knowledge Technol Madrid Spain

In the context of industrial automation, optimising automated guided vehicle (AGV) trajectories is crucial for enhancing operational efficiency and safety. They must travel in crowded work areas and cross narrow corridors with strict safety and time requirements. bio-inspired optimization algorithms have emerged as a promising approach to deal with complex optimization scenarios. Thus, this paper explores the ability of three novel bio-inspired algorithms: the Bat Algorithm (BA), the Whale Optimization Algorithm (WOA) and the Gazelle Optimization Algorithm (GOA);to optimise the AGV path planning in complex environments. To do it, a new optimization strategy is described: the AGV trajectory is based on clothoid curves and a specialised piece-wise fitness function which prioritises safety and efficiency is designed. Simulation experiments were conducted across different occupancy maps to evaluate the performance of each algorithm. WOA demonstrates faster optimization providing suitable safety solutions 4 times faster than GOA. Meanwhile, GOA gives solutions with better safety metrics but demands more computational time. The study highlights the potential of bio-inspired approaches for AGV trajectory optimisation and suggests avenues for future research, including hybrid algorithm development.

关键词： AGV bio-inspired algorithms industry 4.0 optimization

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Evidential-bio-inspired algorithms for modeling groundwater total hardness: A pioneering implementation of evidential neural network for feature selection in water resources management

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ENVIRONMENTAL CHEMISTRY AND ECOTOXICOLOGY 2025年 7卷 494-505页

作者： Usman, Abdullahi G. Jibrin, Abdulhayat M. Mati, Sagiru Abba, Sani I. Near East Univ Operat Res Ctr Healthcare TRNC Mersin 10 TR-99138 Nicosia Turkiye King Fahd Univ Petr & Minerals Civil & Environm Engn Dept Dhahran 31261 Saudi Arabia Near East Univ Fac Pharm Dept Analyt Chem TRNC Mersin 10 TR-99138 Nicosia Turkiye Yusuf Maitama Sule Univ Dept Econ Kano Nigeria Prince Mohammad Bin Fahd Univ Dept Chem Engn Al Khobar 31952 Saudi Arabia Prince Mohammad Bin Fahd Univ Water Res Ctr Al Khobar 31952 Saudi Arabia

Accurate prediction of total hardness (TH) in groundwater is essential for ensuring its suitability for domestic, industrial, and agricultural use. Traditional methods often fail to capture the complex non-linear relationships that characterize water quality data, necessitating more advanced modeling techniques. In this study, three modeling schema batches were developed based on sensitivity analysis results using Evidential Neural Network (EVNN);Batch 1 (B1) include all the parameters, Batch 2 (B2) comprised of electrical conductivity (EC), residual sodium carbonate (RSC), calcium (Ca), chloride (Cl), magnesium (Mg), and nitrate (NO3), while Batch 3 (B3) composed of sulfate (SO4), sodium (Na), bicarbonate (HCO3), sodium adsorption ratio (SAR), potassium (K), groundwater level (GWL), Fluoride (F), pH, and carbonate (CO3) for modeling TH. The dataset was split into a 70:30 ratio for calibration and validation phases, respectively. The sensitivity analysis was subsequently enhanced by bio-inspired algorithms, to predict TH in groundwater, with a specific focus on optimizing feature selection. Sensitivity analysis identified key input features such as EC, RSC, Ca, and Mg as the most influential parameters. The EVNN, coupled with bio-inspired optimization algorithms, specifically the Firefly Algorithm (FA), Invasive Weed Optimization (IWO), and Anti-Bee Colony Optimization (ABC), achieved superior predictive accuracy compared to COVID optimization algorithm (COA). The EVNN-FA-ANN-B2 combination, particularly when using a refined set of features, demonstrated exceptional performance, with a coefficient of determination (R2) of 1.000 and RMSE close to zero. On the other hand, the EVNN-COA-ANN-B1 model exhibited the lowest accuracy, with an R2 of 0.028 and an RMSE of 309.117. This research presents a novel, efficient, and reliable framework for predicting TH in groundwater, offering practical implications for water quality management, especially in regions where high TH le

关键词： Groundwater Physicochemical variables Total hardness Evidential neural network bio-inspired algorithms

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Optimized Resource Allocation in IoT Using Fuzzy Logic and bio-inspired algorithms

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WIRELESS PERSONAL COMMUNICATIONS 2023年第2期131卷 1393-1413页

作者： Sharma, Deepak Kumar Mishra, Jahanavi Singh, Aeshit Govil, Raghav Singh, Krishna Kant Singh, Akansha Indira Gandhi Delhi Tech Univ Women Dept Informat Technol Delhi India Netaji Subhas Univ Technol Dept Informat Technol New Delhi India Amity Univ Uttar Pradesh Dept CSE ASET Noida India Bennett Univ Sch CSET Greater Noida India

IoT smart devices are a confluence of microprocessors, sensors, power source and transceiver modules to effectively sense, communicate and transfer data. Energy efficiency is a key governing value of the network performance of smart devices in distributed IoT networks. Low and discrete power and limited amount of memory and finite number of resources form some major bottlenecks in the workflow. Dynamic load balancing, reliability and flexibility are heavily relied upon by cloud computing for its accessibility. Resources are dynamically provided to the end client in an as-come on-demand fashion with the global network that is the Internet. Proportionally the need for services is increasing at a rate that is astonishing compared to any other forms of development. Load balancing seems a major challenge faced due to the architecture and the modular nature of our cloud environment. Loads need to be distributed dynamically to all the nodes. In this paper, we have introduced a technique that combines fuzzy logic with various nature inspired algorithms-grey wolf algorithm and firefly algorithm to effectively balance the load in a network of IoT devices. The performances of various nature inspired algorithms are compared with a brute force approach based on energy efficiency, network lifetime maximization, node failure rate and packet delivery ratio.

关键词： Load balancing IoT Cloud computing Optimization bio-inspired algorithms Firefly algorithm GWO Fuzzy logic

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Analysis of energy harvesting in SWIPT using bio-inspired algorithms

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INTERNATIONAL JOURNAL OF ELECTRONICS 2023年第2期110卷 291-311页

作者： Nair, Ajin R. Kirthiga, S. Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Coimbatore Coimbatore Tamil Nadu India

In this paper, we consider a power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) system, which is having I and Q (in-phase and quadrature) imbalance hardware impairment. The prime objective of this paper is to maximize the harvested energy for a SWIPT PS system, satisfying a minimum signal to noise ratio (SNR) requirement for information signal processing in the presence of IQ imbalance hardware impairment. The paper uses four types of bio-inspired algorithms like Particle Swarm Optimisation, Artificial Bee Colony, Firefly Algorithm and Invasive Weed Optimisation to attain the maximum harvested energy considering parameters of PS ratio, amplitude and phase imbalances in a Rayleigh fading environment. The simulation results show that under ideal power conversion efficiency and minimum hardware impairments, a maximum SNR of 16.7 dB and harvested energy of 11 dB is achieved for a transmit power of 20 dB in SWIPT systems for IWO bio-inspired algorithms in the 32 QAM modulation scheme.

关键词： Simultaneous wireless information and power transfer (SWIPT) power-splitting IQ imbalance hardware impairments bio-inspired algorithms particle swarm optimization (PSO) artificial bee colony (ABC) firefly algorithm (FA) invasive weed optimization (IWO) energy harvesting

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Comparison of bio-inspired algorithms in a Case Study for Optimizing Capacitor Bank Allocation in Electrical Power Distribution 25

Comparison of Bio-Inspired Algorithms in a Case Study for Op...

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25th International Carpathian Control Conference (ICCC)

作者： Neto, Wolmar Araujo dos Santos, Murillo Ferreira Olivi, Leonardo Rocha Costa, Exuperry Barros Mercorelli, Paolo Fed Univ Espirito Santo UFES Vitoria ES Brazil Fed Ctr Technol Educ CEFET Leopoldina Brazil Fed Univ Juiz De Fora UFJF Juiz De Fora Brazil Leuphana Univ Luneburg Inst Prod Technol & Syst IPTS Luneburg Germany

ISBN: (纸本)9798350350708;9798350350715

This paper investigates the Fish School Search (FSS) algorithm's effectiveness for optimizing capacitor placement in electrical distribution networks, focusing on the challenges of increasing demand and aging infrastructures. The study assesses FSS's unique capability to evade local optima, contrasting its performance with established methods like Ant Colony (AC), Constructive Heuristic Algorithm (CHA), and Genetic Algorithm (GA) in various systems, including IEEE-14, IEEE-33, and IEEE-69 buses. Results highlight FSS's superior performance in reducing active losses, using fewer resources and iterations. This research underscores the potential of bio-inspired algorithms in complex power distribution challenges and suggests new directions for algorithmic efficiency and broader application.

关键词： Capacitor Bank Allocation Electrical Distribution System bio-inspired algorithms

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Optimal Placement of Drone Delivery Stations and Demand Allocation using bio-inspired algorithms

Optimal Placement of Drone Delivery Stations and Demand Allo...

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IEEE International Conference on Smart Mobility (SM)

作者： Elsaid, Feras Sanchez, Enrique Torres Li, Yilun Khamis, Alaa Univ Toronto Civil & Mineral Engn Toronto ON Canada Univ Toronto Elect & Comp Engn Toronto ON Canada Gen Motors Canada Canadian Tech Ctr Oshawa ON Canada

ISBN: (纸本)9798350312751

The last-mile delivery problem has recently received more attention due to dramatic increase in e-commerce and one-day delivery options. One delivery method has been in the forefront of last mile delivery research: drone delivery. Particularly, hybrid truck-drone delivery systems which attempt to overcome the limitations of only using drones. Researchers have attempted to improve drone routing and scheduling, but not many have studied the required infrastructure, including drone stations for recharging and pick-up of packages. This paper tackles the placement of drone delivery stations using bio-inspired optimization algorithms. The solution framework consists of two stages. The first stage tackles the location planning problem of stations, while the second stage deals with the allocation of delivery demand to located stations. The conventional k-means algorithm is used as a baseline for the location planning problem, while the greedy algorithm is used as a baseline for the demand allocation problem. The results shows that the simulated annealing algorithm achieves a 14% reduction in total cost and 6.2x run time improvement, whereas the genetic algorithm achieves a reduction of 10% in total cost and 1.8x run time improvement.

关键词： Last-mile delivery optimal placement delivery drones optimization bio-inspired algorithms

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