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swarm intelligence-based Power Allocation and Relay Selection Algorithm for wireless cooperative network

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2016年第3期10卷 1111-1130页

作者： Xing, Yaxin Chen, Yueyun Lv, Chen Gong, Zheng Xu, Ling Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle swarm Ant Colony Optimization) based on PSACO (Particle swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

关键词： Wireless cooperative communication Power allocation Relay selection swarm intelligence algorithms PSACO

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Intelligent mix design of recycled brick aggregate concrete based on swarm intelligence

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JOURNAL OF BUILDING ENGINEERING 2023年 71卷

作者： Wang, Shiqi Xia, Peng Wang, Zhao Meng, Tao Gong, Fuyuan Zhejiang Univ Coll Civil Engn & Architecture Hangzhou Peoples R China Yokohama Natl Univ Inst Urban Innovat Yokohama Japan

The mechanical properties of recycled brick aggregate concrete (RBAC) are significantly affected by design parameters such as recycled brick aggregate (RBA) replacement ratio and water-cement ratio, which not only increases the difficulty of designing RBAC by experimental or theoretical methods, but also makes the mechanical properties (compressive strength, flexural strength, etc.) of RBAC difficult to match the design requirements. Therefore, an intelligent mix design method for RBAC is proposed in this paper, which can determine the best RBAC mix proportion design scheme according to the design parameters of RBAC (cement content, water-cement ratio and crushed brick ratio, etc.). This method is compared with the performance of 8 machine learning algorithms by creating a database containing 8 input variables. The most suitable model for predicting compressive strength of RBAC is GWO-BP. The predicted/experimental values obtained by the model on the training set and the testing set are 0.98 and 0.96, respectively, and the determination coefficients R2 are 0.97 and 0.99, respectively. SHapley Additive exPlanations (SHAP) is used to analyze the mechanism of design parameters on the compressive strength of RBAC, and the key parameters affecting the compressive strength of RBAC are cement content (Cement) and water-cement ratio (W/C). The Multi-objective optimization (MOO) model is used to target the compressive strength, carbon emission and cost of RBAC, and the scheme with the highest score is determined as the best design scheme of RBAC mix ratio by determining the Pareto optimal solution set of RBAC. The results show that the design method proposed in this paper can effectively guide the mix design of RBAC, the machine learning model optimized by swarm intelligence can effectively predict the compressive strength of RBAC concrete, while the developed MOO model can effectively determine the optimal mix design method.

关键词： Recycled brick aggregate concrete Mix ratio design Machine learning swarm intelligence algorithms Multi -objective optimization

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Optimization based on modified swarm intelligence techniques for a stand-alone hybrid photovoltaic/diesel/battery system

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年 51卷

作者： Maleki, Akbar Shahrood Univ Technol Fac Mech Engn Shahrood Iran

Stand-alone hybrid energy system (SHES) based on photovoltaic/diesel/battery, as one of the main energy resources in remote areas, is widely used for power generation. The optimal sizing and configuration of SHES is essential for achieving a cost-effective and reliable power supply. This article presents five improved particle swarm intelligence algorithms to solve the optimal configuration problem of stand-alone hybrid photovoltaic/ diesel/battery systems. The techniques include five improved particle swarm optimization (PSO) algorithms based on the modified particle velocity and particle position to improve of the local and global searches in all iterations. The objective of the optimization is to determine the optimal sizing of the SHES components (photovoltaic (PV) panels, the diesel power generator, and batteries) to compose a cost-effective and reliable scheme. The improved algorithms are compared by original PSO algorithm. The effects of reliability index, loss of load supply probability, on the optimization of the SHES have been investigated by each algorithm over 30 independent runs. The effect of the maximum number of iteration on the optimization of the hybrid system by the best algorithm for different reliability values is examined. The results show the superiority of the PSO-III in terms of fast convergence, simulation time, and accuracy in comparison to other approachs. Finally, a sensitivity analysis of the SHES shows that the value of total net annual cost (TNAC) is increased around 18% due to the decrease of reliability indexes from 10% to 2%. Also, the mean value of TNAC is decreased around 11.9% due to the increase of the number of iteration from 25 to 175.

关键词： Optimal sizing Stand-alone hybrid photovoltaic diesel battery system swarm intelligence algorithms Cost-effective and reliable system

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Leveraging swarm intelligence for Invariant Rule Generation and Anomaly Detection in Industrial Control Systems

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10705页

作者： Song, Yunkai Huang, Huihui Wang, Hongmin Wei, Qiang Informat Engn Univ Sch Cyberspace Secur Zhengzhou 450007 Peoples R China

Industrial control systems (ICSs), which are fundamental to the operation of critical infrastructure, face increasingly sophisticated security threats due to the integration of information and operational technologies. Conventional anomaly detection techniques often lack the ability to provide clear explanations for their detection, and their inherent complexity can impede practical implementation in the resource-constrained environments typical of ICSs. To address these challenges, this paper proposes a novel approach that leverages swarm intelligence algorithms for the extraction of numerical association rules, specifically designed for anomaly detection in ICS. The proposed approach is designed to effectively identify and precisely localize anomalies by analyzing the states of sensors and actuators. Experimental validation using the Secure Water Treatment (SWaT) dataset demonstrates that the proposed approach can detect over 84% of attack instances, with precise anomaly localization achievable by examining as few as two to six sensor or actuator states. This significantly improves the efficiency and accuracy of anomaly detection. Furthermore, since the method is based on the general control dynamics of ICSs, it demonstrates robust generalization, making it applicable across a wide range of industrial control systems.

关键词： industrial control systems anomaly detection numerical association rules swarm intelligence algorithms security enhancement

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Virtual reference feedback tuning with robustness constraints: A swarm intelligence solution

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ENGINEERING APPLICATIONS OF ARTIFICIAL intelligence 2023年第PartC期123卷

作者： Fiorio, Luan Vinicius Remes, Chrystian Lenon Wheeler, Patrick de Novaes, Yales Romulo Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands Santa Catarina State Univ Dept Elect Engn BR-89219710 Joinville Brazil Univ Fed Parana Dept Elect Engn BR-80060000 Curitiba Brazil Univ Fed Rio Grande do Sul Dept Elect Engn BR-90035190 Porto Alegre Brazil Univ Nottingham Dept Elect & Elect Engn Nottingham NG7 2RD England

The simplified modeling of a complex system allied with a low-order controller structure can lead to poor closed-loop performance and robustness. A feasible solution is to avoid the necessity of a model by using data for the controller design. The Virtual Reference Feedback Tuning (VRFT) is a data-driven design method that only requires a single batch of data and solves a reference tracking problem, although with no guarantee of robustness. In this work, the inclusion of an H & INFIN;robustness constraint to the VRFT cost function is addressed. The estimation of the H & INFIN;norm of the sensitivity transfer function is extended to maintain the one-shot characteristic of the VRFT. swarm intelligence algorithms are used to solve the non-convex cost function. The proposed method is applied in two real-world inspired problems with four different swarm intelligence algorithms, which are compared with each other through a Monte Carlo experiment of 50 executions. The obtained results are satisfactory, achieving the desired robustness criteria.

关键词： Data-driven control Robust control swarm intelligence algorithms Virtual reference feedback tuning

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Discrete Improved Grey Wolf Optimizer for Community Detection

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Journal of Bionic Engineering 2023年第5期20卷 2331-2358页

作者： Mohammad H.Nadimi-Shahraki Ebrahim Moeini Shokooh Taghian Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia Yonsei Frontier Lab Yonsei UniversitySeoulSouth Korea

Detecting communities in real and complex networks is a highly contested topic in network *** many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively fulfill large-scale and real-world ***,this paper presents a new discrete version of the Improved Grey Wolf Optimizer(I-GWO)algorithm named DI-GWOCD for effectively detecting communities of different *** the proposed DI-GWOCD algorithm,I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better *** a novel Binary Distance Vector(BDV)is introduced to calculate the wolves’distances and adapt I-GWO for solving the discrete community detection *** performance of the proposed DI-GWOCD was evaluated in terms of modularity,NMI,and the number of detected communities conducted by some well-known real-world network *** experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon *** comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms.

关键词： Community detection Complex network Optimization Metaheuristic algorithms swarm intelligence algorithms Grey wolf optimizer algorithm

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Multi-trial Vector-based Whale Optimization Algorithm

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Journal of Bionic Engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： swarm intelligence algorithms Metaheuristic algorithms Optimization Engineering design problems Whale optimization algorithm

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Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing

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Journal of Bionic Engineering 2023年第5期20卷 2240-2275页

作者： Jiaochen Chen Zhennao Cai Huiling Chen Xiaowei Chen José Escorcia-Gutierrez Romany F.Mansour Mahmoud Ragab College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Department of Rheumatology and Immunology The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China Department of Computational Science and Electronics Universidad de la CostaCUC080002BarranquillaColombia Department of Mathematics Faculty of ScienceNew Valley University72511El-KhargaEgypt Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing *** assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN *** method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS *** DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 *** addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological *** results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal *** to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation *** research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.

关键词： Multi-threshold image segmentation 2D Rényi entropy Renal pathology Cuckoo search algorithm swarm intelligence algorithms Bionic algorithm

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Adaptive Tuning of Power System Stabilizer Using a Damping Control Strategy Considering Stochastic Time Delay

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IEEE ACCESS 2020年 8卷 124254-124264页

作者： Lala, Jose Antonio Oscullo Gallardo, Carlos Fabian Natl Polytech Sch Fac Elect & Elect Elect Engn Dept Quito 170525 Ecuador

The operation of a modern electrical system presents complex operating conditions, and uncertainty factors increased by the presence of variable and flexible loads. This stochastic operating point variation can promote low-frequency oscillations, and like the conventional power system stabilizer (PSS) features static tuning, it does not provide sufficient damping. Today, an electrical system is monitored through online measurements from the wide-area measurement system (WAMS);moreover, it enables stochastic dynamics to be tracked as the remote signals of WAMS system, the time delay due to the communication channel. It is interesting to analyze the incorporation of the PSS of signals from the WAMS without the time delay effect that degrades the small-signal stability (SSS) and, in turn, allows adaptive tuning. Through two stages, at first, the subspace of the operating point is determined based on a distribution of consumption levels. Each operating point considered a random time delay. For each subspace, establish a bank of tuned PSSs. Second, through the optimal classification and the regression decision tree (CART), the classification rules for the subspace are determined, allowing classifying the measurements of WAMS. The proposed methodology is applied to the New England of 66-bus system and Ecuadorian electric system to demonstrate that the adaptive tuning of PSSs significantly improves SSS under different scenarios and is probabilistic robust, considering the time delay.

关键词： Stochastic power system operating power oscillations adaptive damping control swarm intelligence algorithms wide area damping controller

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Chaotic Coyote Optimization Algorithm

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JOURNAL OF AMBIENT intelligence AND HUMANIZED COMPUTING 2021年第5期13卷 2807-2827页

作者： Tong, Huawei Zhu, Yun Pierezan, Juliano Xu, Youyun Coelho, Leandro dos Santos Nanjing Univ Posts & Telecommun Nanjing 210003 Peoples R China Gannan Normal Univ Ganzhou 341000 Peoples R China Fed Univ Parana UFPR Curitiba Parana Brazil

Coyote Optimization Algorithm (COA) is classified as both swarm intelligence and evolutionary heuristic algorithms. However, getting trapped in a poor local optimum and the low convergence speed are the weaknesses of COA obviously. Due to these weaknesses, this paper proposes a new algorithm named Chaotic Coyote Optimization Algorithm (CCOA) which focusing on COA equipped with chaotic maps. Through utilising ten well-known benchmark functions, experimental results are recorded in tables and drawn in figures to provide a sharp contrast. The performance of CCOA and COA are discussed, which proves CCOA outperforms COA guaranteeing rapid global convergence rate.

关键词： Coyote Optimization Algorithm Chaos theory swarm intelligence algorithms Optimization

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