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A LODBO algorithm for multi-UAV search and rescue path planning in disaster areas

Chinese Journal of Aeronautics 2025年第2期38卷 200-213页

作者： Liman Yang Xiangyu Zhang Zhiping Li Lei Li Yan Shi School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

In disaster relief operations,multiple UAVs can be used to search for trapped *** recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path *** Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above ***,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal *** address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search ***,we expand the search range of the rolling ball dung beetle by using the landmark ***,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local *** verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.

关键词： Unmanned aerial vehicle Path planning meta heuristic algorithm DBO algorithm NP-hard problems

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Whale Optimization-Based Access Control Scheme in D2D Communication Underlaying Cellular Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第3期21卷 3416-3427页

作者： Jadav, Nilesh Kumar Tanwar, Sudeep Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad 382481 India

Integration of device-to-device (D2D) communication has gained significant attention within cellular networks as a means to enhance their capacity, coverage, and performance. Despite these advantages, D2D communication encounters various challenges, such as high interference, resource allocation, energy efficiency, and security. In this paper, we investigate the problem associated with resource allocation in D2D communication underlying cellular networks. The existing resource allocation schemes (e.g., game theory and graph theory) do not offer an access control mechanism, due to which the existing schemes are computationally intensive and do not converge to offer a global optimum solution. Toward this goal, we proposed a whale optimization algorithm(WOA)-based access control scheme to enhance the performance of the resource allocation scheme in D2D communication. In WOA, we created a signal-to-interference-plus-noise ratio (SINR)-based objective function that iteratively discovers the best D2D users, allowing them to participate in the resource allocation process. Moreover, for resource allocation, we adopted the Munkres algorithm, which allows only optimized D2D users (from WOA) to reuse the resources of cellular users (CUs). In the proposed work, WOA acts as an access control scheme that optimally finds the best D2D users and only allows them to reuse cellular resources in the Munkres resource assignment problem. Simulation results show that the proposed scheme significantly improves the system's throughput compared to other existing algorithms. Moreover, other evaluation parameters, such as convergence rate, fairness, WOA update positions, and execution time, show the outperformance of the proposed scheme.

关键词： Whale optimization algorithm evolutionary algorithm D2D communication optimization meta heuristic algorithm Munkres algorithm Whale optimization algorithm evolutionary algorithm D2D communication optimization meta heuristic algorithm Munkres algorithm

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Slime mould algorithm: a comprehensive review of recent variants and applications

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2023年第1期54卷 204-235页

作者： Chen, Huiling Li, Chenyang Mafarja, Majdi Heidari, Ali Asghar Chen, Yi Cai, Zhennao Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou Peoples R China Sichuan Univ Coll Comp Sci Chengdu Peoples R China Birzeit Univ Dept Comp Sci West Bank Palestine King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China

Slime Mould algorithm (SMA) has recently received much attention from researchers because of its simple structure, excellent optimisation capabilities, and acceptable convergence in dealing with various types of complex real-world problems. this study aims to retrieve, identify, summarise and analyse critical studies related to SMA development. Based on this, 98 SMA-related studies in the Web of Science were retrieved, selected, and identified. The two main review vectors were advanced versions of SMAs and application domains. First, we counted and analysed various advanced versions of SMAs, summarised, classified, and discussed their improvement methods and directions. Secondly, we sort out the application domains of SMA and analyse the role, development status, and shortcomings of SMA in each domain. A survey based on the existing literature shows that SMAs clearly outperform some established metaheuristics in terms of speed and accuracy in handling various benchmark problems and solving multiple realistic optimization problems. This review not only suggests possible future directions in the field but, due to the inclusion of graphical and tabular comparisons of various properties, also provides future researchers with a comprehensive source of information about SMA and advanced versions of SAMs and the scope of adaptation for multiple application domains.

关键词： Slime mould algorithm optimization swarm intelligence computational intelligence meta heuristic algorithm

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Multi-Task Learning for Electricity Price Forecasting and Resource Management in Cloud Based Industrial IoT Systems

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IEEE ACCESS 2023年 11卷 54280-54295页

作者： Almazroi, Abdulwahab Ali Ayub, Nasir Univ Jeddah Coll Comp & Informat Technol Khulais Dept Informat Technol Jeddah 21959 Saudi Arabia Capital Univ Sci & Technol Fac Comp Dept Software Engn Islamabad 44000 Pakistan

Cloud computing has gained immense popularity in the logistics industry. This innovative technology optimizes computing operations by eliminating the requirement for physical equipment for calculations. Instead, specialized companies provide cloud-based computing services, relying heavily on computers and servers that consume substantial amounts of energy. Hence, ensuring the availability of affordable and dependable electricity is paramount for the efficient design and management of these logistics services. Cloud centers, which are power-intensive, face the challenge of reducing their energy consumption due to escalating power costs. To address this issue, efficient data placement and node management strategies are commonly employed in logistics operations. An AlexNet model has been designed to optimize storage relocation and predict power prices. The outcome of this initiative has resulted in a considerable reduction in energy consumption at data centres. The model uses Dwarf Mongoose Optimization algorithm (DMOA) to produce an optimal solution for the AlexNet and increase its performance with a real-world dataset from IESO in Ontario, Canada. 75% of the available data was used for training to assure the model's precision, with the remaining 25% allocated to testing purposes. The model forecasts power prices with an MAE of 2.22% and an MSE of 6.33%, resulting in an average reduction of 22.21% in electricity expenses. Our proposed method has an accuracy of 97% compared to 11 benchmark algorithms, including CNN, DenseNet, and SVM having an accuracy of 89%, 88%, and 82%, respectively.

关键词： Cloud systems deep learning energy efficiency energy consumption machine learning meta heuristic algorithm price forecasting logistics

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Power Control of DFIG based WECS using SOSMC and PSO algorithm 2

Power Control of DFIG based WECS using SOSMC and PSO algorit...

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2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)

作者： El Qouarti, Ouassima Essadki, Ahmed Laghridat, Hammadi Nasser, Tamou Mohammed V Univ Ecole Natl Super Arts & Metiers ENSAM Rabat Morocco Mohammed V Univ Ecole Natl Super Arts & Metiers Rabat Morocco Mohammed V Univ Ecole Natl Super Informat & Anal Syst Rabat Morocco

ISBN: (纸本)9781665422093

In recent years many researches went on to explore the deep and powerful impact of evolutionary algorithms on tuning the control parameters of the Doubly Fed Induction Generator (DFIG). The final purpose for that is to optimize controllers' gain and thus improve the systems' robustness and stability. In this article, we focus on Particle Swarm Optimization (PSO) algorithm in order to tune DFIG's parameters;Firstly, a state of art is addressed for articles that have dealt with both PSO and Super-Twisting (ST) algorithms applied on the DFIG based Wind Energy Conversion Systems (WECS). Secondly, a study is conducted on modeling, simulation and control of the DFIG based WECS using ST and PSO algorithms in order to control active and reactive powers. Finally, a comparative study is conducted for different control scenarios under normal and abnormal conditions in order to demonstrate and highlight the impact of each combination on the performances of a grid connected wind turbine generator.

关键词： WECS DFIG optimisation meta heuristic algorithm power control ST PSO

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WOA (Whale Optimization algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve

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ENERGIES 2022年第12期15卷 4456-4456页

作者： Sun, Youzhuang Zhang, Junhua Yu, Zhengjun Liu, Zhen Yin, Pengbo China Univ Petr Coll Earth Sci & Technol Qingdao 266555 Peoples R China Sinopec Shengli Oilfield Dongying 257000 Peoples R China

Porosity is a vital parameter in reservoir research. In the process of oil exploration, reservoir research is very important for oil and gas exploration. Because it is necessary to take cores for indoor test in order to accurately obtain the porosity value of cores, this process consumes significant manpower and material resources. Therefore, this paper introduces the method of machine learning to predict the porosity by using logging curves. This paper creatively develops a WOA (whale optimization algorithm) optimized Elman neural network model to predict porosity through logging parameters PE, DEN, M2R1, AC, GR, R25, R4 and CNL. Porosity measurement is constructed by taking cores for indoor experiments. It contains a total of 328 sample points. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity measured in the laboratory are used as the output parameter. In order to evaluate the performance of the model, RMSE, R-2, MAE and VAF evaluation indexes are introduced to evaluate. This paper also introduces the non-optimized Elman neural network and BP neural network to compare with this optimization model. The research shows that the WOA algorithm optimizes the super parameters of the Elman neural network, so that the performance of the WOA-Elman model is better than the Elman neural network model and the BP neural network model.

关键词： WOA meta heuristic algorithm Elman neural network reservoir prediction

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A Fuzzy Crow Search algorithm for Solving Data Clustering Problem 33rd

A Fuzzy Crow Search Algorithm for Solving Data Clustering Pr...

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33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Wu, Ze-Xue Huang, Ko-Wei Yang, Chu-Sing Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung Taiwan Natl Cheng Kung Univ Inst Comp & Commun Engn Dept Elect Engn Tainan Taiwan

ISBN: (纸本)9783030557881;9783030557898

The crow is one of the most intelligent bird and infamous for observing other birds so that they can steal their food. The crow search algorithm (CSA), a nature-based optimizer, is inspired by the social behavior of crows. Scholars have applied the CSA to obtain efficient solutions to certain function and combinatorial optimization problems. Another popular and powerful method with several real-world applications (e.g., energy, finance, marketing, and medical imaging) is fuzzy clustering. The fuzzy c-means (FCM) algorithm is a critical fuzzy clustering approach given its efficiency and implementation easily. However, the FCM algorithm can be easily trapped in the local optima. To solve this data clustering problem, this study proposes a hybrid fuzzy clustering algorithm combines the CSA and a fireworks algorithm. The algorithm performance is evaluated using eight well-known UCI benchmarks. The experimental analysis concludes that adding the fireworks algorithm improved the CSA's performance and offered better solutions than those by other meta-heuristic algorithms.

关键词： Fuzzy c-means Data clustering Crow search algorithm Fireworks algorithm meta heuristic algorithm

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ViTU-net: A hybrid deep learning model with patch-based LSB approach for medical image watermarking and authentication using a hybrid metaheuristic algorithm

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Computers in Biology and Medicine 2025年 194卷 110393页

作者： V. Nanammal S. Rajalakshmi V. Remya S. Ranjith Department of Electronics and Communication Engineering Jeppiaar Engineering College Chennai India Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai India

In modern healthcare, telemedicine, health records, and AI-driven diagnostics depend on medical image watermarking to secure chest X-rays for pneumonia diagnosis, ensuring data integrity, confidentiality, and authenticity. A 2024 study found over 70 % of healthcare institutions faced medical image data breaches. Yet, current methods falter in imperceptibility, robustness against attacks, and deployment efficiency. ViTU-Net integrates cutting-edge techniques to address these multifaceted challenges in medical image security and analysis. The model's core component, the Vision Transformer (ViT) encoder, efficiently captures global dependencies and spatial information, while the U-Net decoder enhances image reconstruction, with both components leveraging the Adaptive Hierarchical Spatial Attention (AHSA) module for improved spatial processing. Additionally, the patch-based LSB embedding mechanism ensures focused embedding of reversible fragile watermarks within each patch of the segmented non-diagnostic region (RONI), guided dynamically by adaptive masks derived from the attention mechanism, minimizing impact on diagnostic accuracy while maximizing precision and ensuring optimal utilization of spatial information. The hybrid meta-heuristic optimization algorithm, TuniBee Fusion, dynamically optimizes watermarking parameters, striking a balance between exploration and exploitation, thereby enhancing watermarking efficiency and robustness. The incorporation of advanced cryptographic techniques, including SHA-512 hashing and AES encryption, fortifies the model's security, ensuring the authenticity and confidentiality of watermarked medical images. A PSNR value of 60.7 dB, along with an NCC value of 0.9999 and an SSIM value of 1.00, underscores its effectiveness in preserving image quality, security, and diagnostic accuracy. Robustness analysis against a spectrum of attacks validates ViTU-Net's resilience in real-world scenarios.

关键词： Image watermarking meta heuristic algorithm RONI U-net ViT

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Home health care routing and scheduling problem considering temporal dependencies and perishability with simultaneous pickup and delivery

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IFAC-PapersOnLine 2019年第13期52卷 118-123页

作者： Shahnejat-Bushehri, S. Tavakkoli-Moghaddam, R. Momen, S. Ghasemkhani, A. Tavakkoli-Moghaddam, H. School of Industrial Engineering College of Engineering University of Tehran Tehran Iran Arts et Métiers ParisTech LCFC Metz France School of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran

Home health care (HHC) companies provide a wide range of medical and social services to patients in their own homes to help them rehabilitate after illness or injury. Providing high-quality services and reducing operating costs are accessible through proper planning of different sectors of HHC companies. This study presents a mathematical model integrated with real-life constraints to address a home health care routing and scheduling problem (HHCRSP). The planning takes into account temporal precedence and synchronization constraints and limited allowable times for transferring collected biological samples to the laboratory in the pickup and delivery environment. This problem is a new variant of the vehicle routing problem with time windows (VRPTW) with the aim of minimizing the cost related to the transportation and the idle time of caregivers by assigning proper caregivers with required qualifications to patients based on their disjunctive needs. Simulated annealing (SA) and tabu search (TS) are two meta-heuristics applied in two phases for each instance to schedule primary tasks in the first step, and then synchronized services in the next phase. The Solomon's benchmark is used for generating comparative experiments. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Home health care heuristic algorithms Operating costs Patient rehabilitation Pickups Scheduling Service industry Simulated annealing Synchronization Tabu search Vehicle routing Comparative experiments meta heuristic algorithm Perishability Routing and scheduling Simultaneous pickup and deliveries Synchronization constraints Temporal dependencies Vehicle routing problem with time windows

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A Brief Review of the Intelligent algorithm for Traveling Salesman Problem in UAV Route Planning 9

A Brief Review of the Intelligent Algorithm for Traveling Sa...

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IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Xu, Yunpeng Che, Chang Beijing Univ Posts & Telecommun Coll Software Engn Beijing Peoples R China Univ Elect Sci & Technol China Coll Informat & Commun Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781728111902

In the future, unmanned aerial vehicles(UAVs) will be used in a wide range of applications in areas such as wireless communications, logistics, inspections, search and rescue. Path planning in these areas often involves Traveling Salesman Problem (TSP). It is extremely difficult to select the optimal path from a large number of nodes, but it is easy to solve the complex problem after converting it to TSP. The optimization methods for solving TSP include meta heuristic algorithm and fuzzy neural network. With the rapid growth of demand, there is an urgent need for faster and more accurate methods to make UAVs more intelligent. This article reviews the application of UAV route planning in the field of wireless communications and express delivery, and selects a variety of classic and up-to-date algorithms for solving TSP and its variants to promote future researches.

关键词： unmanned aerial vehicles meta heuristic algorithm traveling salesman problem wireless communication neural network fuzzy neural network

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