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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 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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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 Improving Mixed And Incomplete Data Clustering

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IEEE LATIN AMERICA TRANSACTIONS 2018年第8期16卷 2248-2253页

作者： Barroso, E. Villuendas, Y. Yanez, C. Univ Ciego Avila Informat Aplicada Ciego De Avila Cuba Inst Politecn Nacl Ctr Innovac & Desarrollo Tecnol Computo Mexico City DF Mexico Inst Politecn Nacl Ctr Invest Computac Mexico City DF Mexico

This article introduces a novel clustering algorithm for dealing with mixed and incomplete data descriptions of instances: the A(2)FCPAntSA algorithm. Unlike previous proposals, this new algorithm uses groups of instances as initial clusters in an agglomerative clustering scheme. It also merges in a single step, all of the most similar clusters. In addition, the proposal incorporates the use of bio-inspired algorithms to refine the obtained clusters. The numerical experiments carried out over several repository datasets display the superiority of the proposal with respect to other state of the art clustering algorithms, for mixed and incomplete data, by considering several dissimilarity measures and supervised cluster validity indexes.

关键词： clustering mixed and incomplete data bio-inspired algorithms

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bio-inspired algorithms for the optimization of offshore oil production systems

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2012年第10期91卷 1023-1044页

作者： Vieira, Ian Nascimento Leite Pires de Lima, Beatriz Souza Jacob, Breno Pinheiro Univ Fed Rio de Janeiro Polytech Sch BR-21945970 Rio De Janeiro RJ Brazil Univ Fed Rio de Janeiro COPPE Postgrad Inst Civil Engn DeptIlha Fundao BR-21945970 Rio De Janeiro RJ Brazil

This work describes the development, implementation, and assessment of enhanced variants of three different groups of bio-inspired methodologies: genetic algorithms, particle swarm optimization, and artificial immune system. The algorithms are implemented on a computational tool for the synthesis and optimization of offshore oil production risers that connect a floating platform at the sea surface to the wellheads at the sea bottom. Optimization procedures using bio-inspired algorithms for such real-world engineering problems require the calculation of the objective function through a large number of time-consuming finite element nonlinear dynamic analyses, for the evaluation of the structural behavior of each candidate configuration. Therefore, the performance of the algorithms may be measured by the smaller number of objective function evaluations associated to a given target fitness value. The results indicate that the artificial immune system approach, incorporating some enhancements presented in this work, is more effective than the genetic algorithms and particle swarm optimization methods, requiring a smaller number of evaluations to obtain better solutions. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： optimization bio-inspired algorithms offshore systems genetic algorithms particle swarm optimization artificial immune system oil production risers

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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 Mobile Location Management-A New Paradigm 1

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5th International Conference on Frontiers in Intelligent Computing - Theory and Applications (FICTA)

作者： Swayamsiddha, Swati Parija, Smita Singh, Sudhansu Sekhar Sahu, Prasanna Kumar KIIT Univ Sch Elect Engn Bhubaneswar Odisha India NIT Rourkela Rourkela Odisha India

ISBN: (数字)9789811031564

ISBN: (纸本)9789811031564;9789811031557

Mobile location management (MLM) has gained a new aspect in today's cellular wireless communication scenario. It has two perspectives: location registration and location search and a trade-off between the two give optimal cost for location management. An outline of the prominent solutions for the cost optimization in location management using various bio-inspired computations is surveyed. For solving complex optimization problems in various engineering applications more and more such bio-inspired algorithms are recently being explored along with incremental improvement in the existing algorithms. This paper surveys and discusses potential approaches for cost optimization using fifteen bio-inspired algorithms such as Artificial Neural Network, Genetic Algorithm to newly developed Flower Pollination Algorithm and Artificial Plant Optimization. Finally, we survey the potential application of these bio-inspired algorithms for cost optimization in mobile location management issue available in the recent literature and point out the motivation for the use of bio-inspired algorithms in cost optimization and design of optimal cellular network.

关键词： Mobile location management bio-inspired algorithms Location update Paging Optimization

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Optimal UAV Path Planning: Sensing Data Acquisition Over IoT Sensor Networks Using Multi-Objective bio-inspired algorithms

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IEEE ACCESS 2018年 6卷 13671-13684页

作者： Yang, Qin Yoo, Sang-Jo Inha Univ Dept Informat & Commun Engn Incheon 402751 South Korea

The use of unmanned aerial vehicles (UAVs) has been considered to be an efficient platform for monitoring critical infrastructures spanning over geographical areas. UAVs have also demonstrated exceptional feasibility when collecting data due to the wide wireless sensor networks in which they operate. Based on environmental information such as prohibited airspace, geo-locational conditions, flight risk, and sensor deployment statistics, we developed an optimal flight path planning mechanism by using multi objective bio-inspired algorithms. In this paper, we first acquire data sensing points from the entire sensor field, in which UAV communicates with sensors to obtain sensor data, then we determine the best flight path between neighboring acquisition points. Using the proposed joint genetic algorithm and ant colony optimization from possible UAV flight paths, an optimal one is selected in accordance with sensing, energy, time, and risk utilities. The simulation results show that our method can obtain dynamic environmental adaptivity and high utility in various practical situations.

关键词： bio-inspired algorithms multi-objectives optimal path sensor networks unmanned aerial vehicle

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