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Comprehensive performance metric for bio-inspired optimizers: a generation-wise population convergence approach toward global optimality

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Iran Journal of Computer Science 2025年 1-49页

作者： Thapliyal, Shivankur Kumar, Narender Doon University Dehradun India

Metaheuristic algorithms revolutionize optimization, offering flexibility, scalability, and problem-solving expertise. They excel in diverse domains due to adaptability, parallelism, and global exploration, making them essential in modern computing. Researchers have created numerous metaheuristics, necessitating benchmarking and comparisons to comprehend their capabilities and limitations, considering the “No Free Lunch Theorem”. Despite their prominence, the fundamental questions regarding performance difficulties are only partially answered due to a lack of in-depth analytical analysis. Due to the limitations of conventional metrics such as convergence rate and the quality of the final solution, which often do not provide a comprehensive understanding of an algorithm’s performance and can sometimes be confusing or challenging to interpret. This study introduces a new performance metric that monitors the algorithm’s behavior, including exploration, exploitation, and convergence, across iterations while considering data from all individuals in the population to rectify these limitations. The evaluation of the proposed matrices involves three stages. First, the performance of ten metaheuristic algorithms is analyzed using the proposed metric on 23 benchmark functions with dimensions of 30, 100, 500, and 1000, as well as 30 functions from IEEE CEC-2017 with dimensions of 30 and 15 functions from IEEE CEC-2015 with dimensions of 10. Second, the metric is applied to assess multi-layer perceptron adaptations on eight manifold datasets. Finally, graphical and statistical tools, including error trajectory plots, rating matrices, and Friedman ranking tests, are utilized to provide deeper insights. The results demonstrate the metric’s effectiveness in evaluating the exploration, exploitation, and convergence abilities of the algorithms. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Benchmark functions bio-inspired algorithms Metaheuristics Nature-inspired algorithms Optimization Particle swarm optimization Performance metric Swarm algorithms

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Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms

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biomimetic Intelligence & Robotics 2024年第4期4卷 76-91页

作者： Timothy Sellers Tingjun Lei Chaomin Luo Zhuming Bi Gene Eu Jan Department of Electrical and Computer Engineering Mississippi State UniversityStarkville 39762USA Department of Civil and Mechanical Engineering Purdue University Fort WayneFort Wayne 46805USA Department of Computer Science Asia UniversityTaichung 413305TaiwanChina

In the field of autonomous robots,achieving complete precision is challenging,underscoring the need for human intervention,particularly in ensuring *** Autonomy Teaming(HAT)is crucial for promoting safe and efficient human-robot collaboration in dynamic indoor *** paper introduces a framework designed to address these precision gaps,enhancing safety and robotic interactions within such *** to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram(GVD)with spatio-temporal graphs,effectively combining human feedback,environmental factors,and key *** integral component of this system is the improved Node Selection Algorithm(iNSA),which utilizes the revised Grey Wolf Optimization(rGWO)for better adaptability and ***,an obstacle tracking model is employed to provide predictive data,enhancing the efficiency of the *** insights play a critical role,from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental *** simulation and comparison tests confirm the reliability and effectiveness of our proposed model,highlighting its unique advantages in the domain of *** comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.

关键词： Human autonomy teaming(HAT) Robot path planning Generalized Voronoi diagram(GVD) Spatio-temporal graphs bio-inspired algorithms

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bio-inspired optimization methods for visible light communication: a comprehensive review

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第8期58卷 1-25页

作者： Dratnal, Martin Danys, Lukas Martinek, Radek VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Cybernet & Biomed Engn 17 Listopadu 2172-15 Ostrava 70800 Czech Republic

Visible light communication (VLC) offers a promising alternative to traditional radio frequency communication due to its greater bandwidth, energy efficiency, and security advantages. This paper presents a comprehensive review of bio-inspired optimization algorithms, including swarm intelligence and genetic algorithms, that enhance the performance and robustness of VLC systems. These techniques have demonstrated significant potential in addressing challenges such as channel optimization and noise reduction. However, despite their advantages, bio-inspired algorithms also face limitations, including computational complexity and limited adaptability to dynamic real-world conditions. Additionally, the integration of bio-inspired methods with artificial intelligence (AI) may further enhance their adaptability and efficiency in VLC systems. This review highlights both the opportunities and challenges associated with bio-inspired optimization in VLC and provides insights into future directions for research and practical implementation, which will focus on developing more efficient and scalable bio-inspired approaches that can operate in highly variable environments while minimizing energy consumption.

关键词： Artificial intelligence bio-inspired algorithms Channel equalization Genetic algorithm Swarm intelligence Visible light communication.

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Towards Enhancing Computational Complexity in Explainable Decision Support System: Exploring bio-inspired Approaches

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Procedia Computer Science 2025年 258卷 430-442页

作者： Ijeoma Noella Ezeji Matthew Adigun Kayode Oki University of Zululand University of Zululand Kwadlagezwa South Africa Department of Information Technology Walter Sisulu Univesrsity East London South Africa

Decision support systems play an important role in aiding decision-making processes by providing valuable insights and recommendations driven by data analysis. However, as decision support systems increasingly employ complex Artificial Intelligence (AI) techniques, the need for explainability becomes essential to build trust and transparency with users. As a result, explainable AI (XAI) has emerged as a paradigm that seeks to make AI decision-making processes transparent and understandable to humans. While explainability enhances user comprehension, it introduces computational complexity challenges. In the evolving landscape of Explainable Decision Support Systems (XDSS), the quest for optimizing computational complexity without sacrificing explainability presents a challenge. This paper delves into the potential of bio-inspired algorithms as a novel approach to addressing this challenge. Through a comprehensive theoretical analysis, this paper explores various bio-inspired algorithms, such as Genetic algorithms, Particle Swarm Optimization, and Ant Colony Optimization, to assess their applicability and effectiveness in enhancing the computational efficiency of XDSS. By establishing a set of selection criteria to identify the most promising bio-inspired algorithm for optimizing computational complexity in XDSS. The paper sets the groundwork for future research by highlighting theoretical foundations, potential applications, and a clear direction for empirical validation. This exploration not only contributes to the theoretical advancement in the field of XDSS but also opens new avenues for practical implementations that can significantly improve explainable decision support systems’ performance and usability.

关键词： bio-inspired algorithms Computational Complexity Explainable Decision Support System (XDSS) Explainable AI Optimization techniques

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Using bio-inspired Features Selection algorithms in Software Effort Estimation: A Systematic Literature Review 45

Using Bio-inspired Features Selection Algorithms in Software...

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45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) / 22nd Euromicro Conference on Digital System Design (DSD)

作者： Ali, Asad Gravino, Carmine Univ Salerno Dept Comp Sci Via Giovanni Paolo II I-84084 Fisciano SA Italy

ISBN: (纸本)9781728134215

Feature selection algorithms select the best and relevant set of features of the datasets which leads to an increase in the accuracy of predictions when employed with the machine learning techniques. Different feature selection algorithms are used in the domain of Software Development Effort Estimations (SDEE) and recently the use of bio-inspired feature selection algorithms got the attention of the researchers, which provided the best results in terms of the accuracy measures. In this paper, we manage to systematically evaluate and assess different bio-inspired feature selection algorithms which have been employed and investigated in the studies related to SDEE with the aim of increasing the accuracy of estimations. To the best of our knowledge, there is no Systematic Literature Review (SLR) which investigated the use of bio-inspired algorithms in SDEE. Since, the use of bio-inspired algorithms in the area of SDEE started in the late 2000, we have considered the studies published between 2007-2018. We have selected about 30 different studies from five digital libraries, i.e., IEEE explore, Springer, ScienceDirect, ACM digital library, and Google Scholar, after the filtering of inclusion/exclusion and quality assessment criteria. The main findings of our SLR are that Genetic algorithms (GA) and Particle Swarm Optimizations (PSO) are widely used bio-inspired algorithms. Moreover, GA and PSO are the algorithms which outperform baseline estimation techniques (estimation techniques employed without any feature selection algorithms) in more number of experiments, in terms of prediction accuracy.

关键词： Effort estimation Feature selection algorithms bio-inspired algorithms Systematic Literature Review

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Optimization of the tensile strength of friction stir welded heat treatable aluminum alloy by using bio-inspired artificial intelligence algorithms

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FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY 2022年第62期16卷 448-459页

作者： Mishra, Akshansh Dasgupta, Anish Politecn Milan Sch Ind & Informat Engn Milan Italy Artificial Intelligence Anal Cognizant Technol Solut Kolkata India

The concepts and inspiration of biological evolution in nature are used to create new and effective competing tactics in the burgeoning field of bio-inspired computing optimization algorithms. In the present work, nine specimens of similar alloys i.e., AA6262 were Friction Stir Welded. Tool Rotational Speed (RPM), Traverse Speed (mm/min), and Plunge Depth (mm) were the input parameters while the Ultimate Tensile Strength (MPa) was an output parameter. The main objective of the work is to obtain the maximum optimized Ultimate Tensile Strength (MPa) by using bio-inspired Artificial Intelligence algorithms i.e., Differential Evolution and Max Lipschitz optimization (Max LIPO) Algorithm. The results showed that the Differential Evolution algorithm resulted in a slightly higher value of the Ultimate Tensile Strength of 184.87 MPa in comparison to the Max LIPO algorithm which resulted in the Ultimate Tensile Strength value of 183.94 MPa.

关键词： Artificial Intelligence bio-inspired algorithms Friction Stir Welding Ultimate Tensile Strength Optimization

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Co-simulation of bio-inspired multi-agent algorithms 20

Co-simulation of bio-inspired multi-agent algorithms

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Proceedings of the 2020 Summer Simulation Conference

作者： Cinzia Bernardeschi Andrea Domenici Maurizio Palmieri Adriano Fagiolini University of Pisa Pisa Italy University of Palermo Palermo Italy

ISBN: (纸本)9781713814290

This paper reports on the co-simulation of a team of robots deployed in an exploration task, coordinated by a bio-inspired exploration algorithm. The co-simulation integrates the high-level exploration algorithm with detailed implementations of the robot controllers and kinematic models. Co-simulation results are used to find and correct mismatches between submodels.

关键词： map exploration cyber-physical systems co-simulation bio-inspired algorithms

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Summarizing social media content via bio-inspired influence maximization algorithms 2020

Summarizing social media content via bio-inspired influence ...

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The 9th International Conference on Smart Media and Applications

作者： Christian Esposito Vincenzo Moscato Giancarlo Sperli Chang Choi University of Salerno Italy University of Naples Federico II italy Gachon University S. Korea

ISBN: (纸本)9781450389259

In this paper, we describe a multimedia summarization technique for Online Social Networks (OSNs) using a bio-inspired influence maximization algorithm. As first step, we model each OSN using an hypergraph based approach that the authors have presented in some previous works. Then, we leverage an influence analysis methodology based on the bees' behaviors within an hive to determine the most important multimedia objects with respect to one or more topics of interest. Finally, a summarization technique is exploited to determine from the list of candidates a multimedia summary in according to a model that favors priority (w.r.t. some user keywords), continuity, variety and not repetitiveness features. Several preliminary experiments on Flickr dataset show the effectiveness of the proposed summarization approach and encourage future work.

关键词： bio-inspired algorithms

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bioinspired small language models in edge systems for bee colony monitoring and control

Internet of Things (The Netherlands)

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Internet of Things (The Netherlands) 2025年 32卷

作者： Juan M. Nún̄ez, V. Giraldo, Diana M. Segura, Sebastián Gómez Corchado, Juan M. Prieta, Fernando De la BISITE Research Group University of Salamanca Edificio Multiusos I+D+I Salamanca 37007 Spain iDMI Research Group Universidad Autónoma de Occidente Cll 25 - 115-85 Km 2 Vía Cali – Jamundí Valle del Cauca Cali 760030 Colombia GIACMAP Research Group Fundación Centro Colombiano de Estudios Profesionales Cl. 9b # 29a - 67 La Alameda Valle del Cauca Cali 760042 Colombia

This paper proposes a hybrid IoT architecture based on Generative Artificial Intelligence (Gen-AIoT) for the intelligent monitoring and control of beehives, designed with processing capabilities both at the edge and in the cloud, thus adapting to environments with or without internet connectivity. Through an IoT sensor network, the system collects critical data on environmental parameters and hive conditions, such as temperature, humidity, wind speed, and hive weight, processing them locally at the edge or centrally in the cloud. The architecture incorporates a recommendation system that uses a small language model (SLM) to generate real-time alerts based on data provided by the IoT sensors. This system implements two distinct SLM models, Phi-3.5 and Tinyllama, enabling hardware performance measurement and optimizing efficiency for edge processing. To establish optimal environmental ranges, the recommendation system uses bio-inspired algorithms, such as ant colony optimization, genetic algorithms, and bee swarm algorithms. Additionally, LSTM neural networks are included to predict honey production and plan hive placement based on climate and weight projections, allowing for precise and personalized adjustments. This dual processing capability (edge and cloud) reduces the need for human intervention, optimizes hive inspection times, and minimizes false positives in monitoring, making it especially beneficial for large-scale beekeeping, where weekly inspection times can exceed 50 h. With this architecture, inspection time is reduced by 80%, significantly improving efficiency in hive management and promoting sustainable practices for bee conservation through intelligent agriculture. © 2025 The Authors

关键词： Beehives bio-inspired algorithms Edge AIoT GenAI

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From ants to whales: metaheuristics for all tastes

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ARTIFICIAL INTELLIGENCE REVIEW 2020年第1期53卷 753-810页

作者： Fausto, Fernando Reyna-Orta, Adolfo Cuevas, Erik Andrade, Angel G. Perez-Cisneros, Marco Univ Guadalajara CUCEI Dept Elect Ave Revoluc 1500 Guadalajara 44430 Jalisco Mexico Univ Autonoma Baja California Fac Ingn Blvd Benito Juarez Mexicali 21280 Baja California Mexico

Nature-inspired metaheuristics comprise a compelling family of optimization techniques. These algorithms are designed with the idea of emulating some kind natural phenomena (such as the theory of evolution, the collective behavior of groups of animals, the laws of physics or the behavior and lifestyle of human beings) and applying them to solve complex problems. Nature-inspired methods have taken the area of mathematical optimization by storm. Only in the last few years, literature related to the development of this kind of techniques and their applications has experienced an unprecedented increase, with hundreds of new papers being published every single year. In this paper, we analyze some of the most popular nature-inspired optimization methods currently reported on the literature, while also discussing their applications for solving real-world problems and their impact on the current literature. Furthermore, we open discussion on several research gaps and areas of opportunity that are yet to be explored within this promising area of science.

关键词： Nature-inspired metaheuristics bio-inspired algorithms Optimization review

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