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nature-inspired optimization algorithms for community detection in complex networks: a review and future trends

TELECOMMUNICATION SYSTEMS 2020年第2期74卷 225-252页

作者： Abduljabbar, Dhuha Abdulhadi Hashim, Siti Zaiton Mohd Sallehuddin, Roselina UTM Fac Engn Sch Comp Utm Skudai 81310 Johor Malaysia Baghdad Univ Dept Comp Sci Baghdad Iraq

Over the past couple of decades, the research area of network community detection has seen substantial growth in popularity, leading to a wide range of researches in the literature. nature-inspired optimization algorithms (NIAs) have given a significant contribution to solving the community detection problem by transcending the limitations of other techniques. However, due to the importance of the topic and its prominence in many applications, the information on it is scattered in various journals, conference proceedings, and patents, and lacked a focused-literature that synthesizes them in a single document. This review aims to provide an overview of the NIAs and their role in solving community detection problems. To achieve this goal, a systematic study is performed on NIAs,followed by historical and statistical analysis of the researches involved. This would lead to the identification of future trends, as well as the discovery of related research challenges. This review provides a guide for researchers to identify new areas of research, as well as directing their future interest towards developing more effective frameworks in the context of nature-inspired community detection algorithms.

关键词： Community detection Metaheuristic nature-inspired optimization algorithms Complex networks

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Advanced internet of things enhanced activity recognition for disability people using deep learning model with nature-inspired optimization algorithms

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Scientific Reports 2025年第1期15卷 1-19页

作者： Mohammed Maray Department of Information Systems College of Computer Science King Khalid University Abha Saudi Arabia King Salman Center for Disability Research Riyadh 11614 Saudi Arabia

Human activity recognition has complex applications because of its worldly use of acquisition devices, namely video cameras and smartphones, and its capability to take human activity data. Human activity recognition became a hot scientific subject in the area of computer vision. It is convoluted in the growth of numerous significant applications like virtual reality, human-computer interaction, video surveillance, home monitoring, and security. Then, a broad range of activity recognition models is established for disabled individuals. Human activity recognition is recognized as the art of naming and identifying activities utilizing artificial intelligence-based deep learning and machine learning methods. In this manuscript, an Enhanced Activity Recognition for Disability People Using a Deep Learning Model and nature-inspired optimization algorithms (EARDP-DLMNOA) model is proposed. The EARDP-DLMNOA model mainly relies on improving the activity recognition model using advanced optimization algorithms. Initially, the data normalization stage is executed using the min-max normalization to convert input data into a beneficial format. Furthermore, the EARDP-DLMNOA model employs the adaptive chimp optimization (AdCO) technique for the feature subset selection. The deep convolutional auto-encoder (DCAE) technique categorizes data into predefined classes based on its features for the activity recognition process. Finally, the DCAE model’s hyperparameter selection uses the zebra optimization algorithm (ZOA) model. A wide-ranging experimentation is carried out to validate the performance of the EARDP-DLMNOA approach under the HAR through the smartphone dataset. The experimentation validation of the EARDP-DLMNOA approach portrayed a superior accuracy value of 97.58% over existing methods.

关键词： Activity recognition Data normalization Deep learning Disability people Internet of things nature-inspired optimization algorithms

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A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

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Frontiers of Structural and Civil Engineering 2024年第1期18卷 30-50页

作者： Bin XI Ning ZHANG Enming LI Jiabin LI Jian ZHOU Pablo SEGARRA Department of Civil and Environmental Engineering Politecnico Di MilanoMilano 20133Italy Leibniz Institute of Ecological Urban and Regional Development(IOER) Dresden 01217Germany Universidad Politécnica de Madrid-ETSI Minas y Energía Madrid 28003Spain Department of Civil Engineering KU Leuven CampusBrugge 8200Belgium School of Resources and Safety Engineering Central South UniversityChangsha 410083China

The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic ***,RA concrete is plagued with considerable durability concerns,particularly *** advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is *** the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to *** the other hand,carbonation is influenced by many factors and is hard to *** this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA *** types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive *** was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R^(2) value of 0.9949 and 0.9398 for training and testing sets,***-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were *** also showed better generalization capabilities when compared with different models in the ***,this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.

关键词： recycled aggregate concrete carbonation depth nature-inspired optimization algorithms extreme gradient boosting technique parametric analysis

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Attraction and diffusion in nature-inspired optimization algorithms

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 1987-1994页

作者： Yang, Xin-She Deb, Suash Hanne, Thomas He, Xingshi Middlesex Univ Sch Sci & Technol Hendon Campus London NW4 4BT England Cambridge Inst Technol Ranchi 835103 Jharkhand India Univ Appl Sci & Arts Northwestern Switzerland Riggenbachstr 16 CH-4600 Olten Switzerland Xian Polytech Univ Sch Sci 19 Jinhua South Rd Xian Shaanxi Peoples R China

nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behavior and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms and then point out some key topics for further research.

关键词： Metaheuristics nature-inspired optimization algorithms Analysis of algorithms Attraction Diffusion

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Online auto-tuning of multiresonant current controller with nature-inspired optimization algorithms and disturbance in the loop approach

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APPLIED SOFT COMPUTING 2023年 144卷

作者： Tarczewski, Tomasz Stojic, Djordje Szczepanski, Rafal Niewiara, Lukasz Grzesiak, Lech M. Hu, Xiaosong Nicolaus Copernicus Univ Inst Engn & Technol Grudziadzka 5 Str PL-87100 Torun Poland Univ Belgrade Elect Inst Nikola Tesla K Glavin 8a Str Belgrade Serbia Warsaw Univ Technol Inst Control & Ind Elect Koszykowa 75 Str PL-00662 Warsaw Poland Chongqing Univ Automot Engn Chongqing 47913 Peoples R China

This paper presents a novel tuning method of a complex multiresonant current controller (MRCC). An online auto-tuning process is proposed to provide optimal coefficients suitable for the real-time operation of a grid-connected power converter. The time-consuming and inaccurate simulation stage based on the model of the plant is omitted. A novel function of constraints has been introduced into the optimization scheme to ensure the desired behavior of the control system. In this solution, the noise level of a control signal and the rise time of controlled currents are directly defined. To the author's best knowledge, it is the first time when such a solution is proposed. Next, the most commonly used meta-heuristic optimization algorithms were applied to solve this particular optimization problem. Another original concept presented in the paper is the application of the disturbance-in-the-loop (DiL) approach. In this method, the distorted phase voltages are emulated during the tuning stage, and optimal coefficients of MRCC are selected in the off-grid operation of the power converter. The proposed solution ensures safe and efficient online tuning of the MRCC for an extremely simple performance index. As a result, the high-performance operation of the grid-connected inverter is achieved. A brief stability analysis based on Lyapunov's stability theory is included. Experimental results obtained for grid-connected inverter indicate superior disturbance attenuation and current tracking. Advantages of the proposed approach are exhibited when compared to two analytical reference solutions.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Auto-tuning Multiresonant current controller (MRCC) Disturbance-in-the-loop (DiL) nature-inspired optimization algorithms Function of constraints Grid-connected inverter

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The application of nature-inspired optimization algorithms on the modern management: A systematic literature review and bibliometric analysis

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JOURNAL OF MANAGEMENT & ORGANIZATION 2023年第4期29卷 655-678页

作者： Zhou, Yi Xia, Weili Dai, Jiapeng Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China Nanjing Univ Sch Govt Nanjing 210023 Peoples R China

With the expanding adoption of technology and intelligent applications in every aspect of our life, energy, resource, data, and product management are all improving. So, modern management has recently surged to cope with modern societies. Numerous optimization approaches and algorithms are used to effectively optimize the literature while taking into account its many restrictions. With their dependability and superior solution quality for overcoming the numerous barriers to generation, distribution, integration, and management, nature-inspired meta-heuristic optimization algorithms have stood out among these methods. Hence, this article aims to review the application of nature-inspired optimization algorithms to modern management. Besides, the created clusters introduce the top authors in this field. The results showed that nature-inspired optimization algorithms contribute significantly to cost, resource, and energy efficiency. The genetic algorithm is also the most important and widely used method in the previous literature.

关键词： Data management energy management modern management nature-inspired optimization algorithms organization management product management resource management

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Multi-stage optimization of the installation of Energy Storage Systems in railway electrical infrastructures with nature-inspired optimization algorithms

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 104卷 104370-104370页

作者： Roch-Dupre, David Gonsalves, Tad Cucala, Asuncion P. Pecharroman, Ramon R. Lopez-Lopez, Alvaro J. Fernandez-Cardador, Antonio Comillas Pontifical Univ Madrid Inst Res Technol Fac Econ & Business Adm Madrid Spain Sophia Univ Chiyoda City Tokyo Japan Comillas Pontifical Univ madrid Inst Res Technol Madrid Spain

Installing Energy Storage Systems (ESSs) to improve electrical infrastructures of Direct-current (DC) railway systems implies considerable investments that must be assessed carefully. Therefore, it is often necessary to combine detailed railway simulations and decision support mechanisms. Unfortunately, most examples in the literature deal with this topic applying only a single-stage optimization approach: the whole installation is undertaken in a single step, assuming the total budget is available. This paper presents a comprehensive methodology to assess the gradual deployment of the installations when the budget is split into different time periods. This approach is a common situation in real projects and has not been studied yet in the literature. Most often, this type of multi-stage problem is tackled by optimizing each stage independently. On the contrary, this paper proposes to take decisions considering the global impact of each stage optimization, rendering a more efficient solution. This paper proposes a multi-stage formulation of two nature-inspired optimization algorithms (Genetic and Fireworks) to address the installation of ESSs in a realistic railway line. Results demonstrate the excellent behavior of the proposed multi-stage optimization.

关键词： optimization of Energy Storage Systems nature-inspired optimization algorithms Railway simulation Railway power systems Energy efficiency

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Automatic Data Clustering Framework Using nature-inspired Binary optimization algorithms

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IEEE ACCESS 2021年 9卷 93703-93722页

作者： Merikhi, Behnaz Soleymani, M. R. Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

Cluster analysis using metaheuristic algorithms has earned increasing popularity over recent years due to the great success of these algorithms in finding high-quality clusters in complex real-world problems. This paper proposes a novel framework for automatic data clustering with the capability of generating clusters with approximately the same maximum distortion using nature-inspired binary optimization algorithms. The inherent problem with clustering using such algorithms is having a huge search space. Therefore, we have also proposed a binary encoding scheme for the particle representation to alleviate this problem. The proposed clustering solution requires no prior knowledge of the number of clusters and proceed with the process based on re-clustering, merging, and modifying the small clusters to compensate for the distortion gap between groups with different sizes. The proposed framework's performance has been evaluated over a wide range of synthetic, real-life, and higher dimensional datasets first by considering four different binary optimization algorithms for the optimizer module. Then, it has also been compared to multiple classical and new clustering solutions and two other automatic clustering techniques in continuous search space in terms of separation and compactness of the clusters by utilizing internal validity measures. The experimental results show the proposed solution is highly efficient in creating well-separated and compact clusters with approximately the same distortion in most datasets. Moreover, the application of the proposed framework to the correlated binary dataset is also reported as a case study. The presence of correlation in a dataset results from the similarity between data points in the same category, such as repeated measurements in remote sensing, crowdsourced multi-view video uploading, and augmented reality. Simplicity, customizability, and flexibility in adding extra conditions to the proposed solution and having a dynamic nu

关键词： Clustering algorithms optimization Partitioning algorithms Distortion Approximation algorithms Particle swarm optimization Heuristic algorithms Automatic data clustering binary search space binary clustering cluster-level constraint distortion dynamic clusters nature-inspired optimization algorithms

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Integrating nature-inspired refined red-tailed hawk algorithm with enhanced time-series dense encoder for seismic event prediction

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EARTH SCIENCE INFORMATICS 2025年第4期18卷 1-21页

作者： Kumari, Priyanka Kumar, Sunil Giri, Ram Kumar Sheshma, Sanju Kumari Singh, Bhupesh Kumar Amity Univ Rajasthan Amity Sch Engn & Technol Jaipur India MoES India Meteorol Dept Lodi Rd New Delhi 110003 India Rajasthan Tech Univ Kota India

Earthquake and tsunami forecasting are critical components of disaster preparedness and mitigation efforts. This paper presents a novel HawkTide ProForecast model that integrates the Refined Red-Tailed Hawk (RR-TH) algorithm for feature optimization and the Enhanced Time-series Dense Encoder (ETiDE) model for forecasting seismic events and tsunamis. The RR-TH algorithm mimics the hunting behavior of hawks to efficiently select the most relevant features from seismic data, enhancing the model's capacity to seize essential patterns. The ETiDE model, known for its accuracy in time-series forecasting, utilizes dense encoding techniques to capture intricate temporal patterns and dependencies in sequential data streams. To evaluate the proposed model, we use standard metrics including precision, accuracy, recall, and F1 score. These metrics provide the model's performance in predicting earthquake alert levels and tsunami possibilities. The model is trained and tested on historical seismic data to demonstrate its effectiveness in real-world scenarios. Our experimental results show that the integrated model outperforms traditional methods in terms of prediction accuracy and reliability. The precision, accuracy, recall, and F1 score metrics demonstrate the model's ability to accurately forecast seismic events and tsunamis, highlighting its potential for improving early warning systems and disaster response strategies.

关键词： Earthquake and Tsunami forecasting nature-inspired optimization algorithms

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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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