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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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An adaptive control study for a DC motor using meta-heuristic algorithms

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IFAC-PapersOnLine 2017年第1期50卷 13114-13120页

作者： Rodríguez-Molina, Alejandro Villarreal-Cervantes, Miguel Gabriel Aldape-Pérez, Mario Departamento de Posgrado LGAC de Mecatrónica Instituto Politécnico Nacional CIDETEC CDMX Av. Juan de Dios Bátiz s/n 07700 Mexico

In this work, a comparative study of the use of different meta-heuristic techniques in the adaptive control for the speed regulation of the DC motor with parameters uncertainties is presented. Several adaptive controllers based on the optimizers of Differential Evolution (DE), Particle Swarm Optimization (PSO), Bat algorithm (BAT), Firefly algorithm (FFA) and Wolf Search algorithm (WSA) are proposed in order to on-line tune the parameters of the DC motor. These parameters are used in calculating the control signal. Simulations show the efficacy of each control strategy. Given the results, the controller based on PSO is one of the most promising alternatives for this approach. © 2017

关键词： Electric machine control Adaptive control systems Controllers DC motors heuristic algorithms heuristic methods Parameter estimation Particle swarm optimization (PSO) Adaptive Control Differential Evolution heuristics meta heuristic algorithm meta heuristic techniques Optimization problems Output regulation Parameters uncertainties

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A peak-over-threshold search method for global optimization

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AUTOMATICA 2018年 89卷 83-91页

作者： Gao, Siyang Shi, Leyuan Zhang, Zhengjun City Univ Hong Kong Dept Syst Engn & Engn Management Tat Chee Ave Kowloon Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China Univ Wisconsin Dept Ind & Syst Engn 1513 Univ Ave Madison WI 53706 USA Univ Wisconsin Dept Stat 1300 Univ Ave Madison WI 53706 USA

In this paper, we propose a random search method, called peak-over-threshold search (POTS), for solving global optimization problems. An important feature of POTS is that it combines the existing partition based random search framework (e.g., Shi and Olafsson 2000a: Chen et al. 2011) with the peak-over threshold statistical reference (Coles, 2001) in order to achieve high search efficiency. In each iteration, POTS partitions the solution space into several subregions, evaluates the quality of each subregion and moves to promising subregions for more partitioning and sampling. To effectively assess the quality of a subregion, an extreme value type of inference in statistics is used to develop a new promising index which reflects the optimal objective value of a subregion and biases the search to regions that are likely to contain the optimal or near-optimal solutions. Under assumptions on the depth of partitioning and the probability of correct movement, POTS is shown to converge with probability one to the optimal region. The higher efficiency of the proposed method is illustrated by numerical examples. The application of POTS to beam angle selection, an important optimization problem in radiation treatment, is also presented in this paper. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Partition-based random search Global optimization Extreme value analysis meta heuristic algorithm

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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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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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A Multi-Depot Home Care Routing Problem with Time Windows and Fuzzy Demands Solving by Particle Swarm Optimization and Genetic algorithm

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IFAC-PapersOnLine 2018年第11期51卷 358-363页

作者： Tohidifard, M. Tavakkoli-Moghaddam, R. Navazi, F. Partovi, M. School of Industrial Engineering College of Engineering University of Tehran Tehran Iran

Providing sanitarian and medical treatments for patients in their location has lots of benefits. Having ordered and on-time medical services cause faster recovery for patients, besides reducing their expenditure. This study presents a new mathematical model for a multi-depot vehicle routing problem (VRP) with time windows for home health care firms. The multi-depot VRP tries to visit some patients and give necessary services to them by several vehicles settled at multiple depots. The endeavor of this problem is raising the satisfaction of patients while minimizing the distance and time of the traveling tour, the number of vehicles, and the transportation cost in a patient's hard time windows. The problem is solved by genetic algorithm (GA) and particle swarm optimization (PSO). The related results are compared with the GAMS outcomes for small-sized problems. © 2018

关键词： Particle swarm optimization (PSO) Genetic algorithms heuristic algorithms Home health care Patient rehabilitation Routing algorithms Vehicle routing Vehicles Medical services Medical treatment meta heuristic algorithm Multi depot vehicle routing problems New mathematical model Number of vehicles Routing problems Transportation cost

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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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Mobile Robot Path Planning based on Conformal Geometric Algebra and Teaching-Learning Based Optimization

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IFAC-PapersOnLine 2018年第13期51卷 338-343页

作者： Hernandez-Barragan, Jesus University of Guadalajara CUCEI Computer Science Department United States

A crucial task for mobile robots is to move safely through its environment. Therefore, the determination of a safe path planning algorithm is a key problem. In this paper, the authors present a path planning approach based on Conformal Geometric Algebra and Teaching-Learning Based Optimization. The proposed approach solves the path planning for 2D and 3D environments. The simulation results proved the effectiveness of the proposed method. © 2018

关键词： Motion planning Algebra Geometry Mobile robots Optimization Robot programming 3 D environments Conformal Geometric Algebra Geometric Algebra meta heuristic algorithm Robot path planning Safe path planning Teaching learning based optimizations

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LabVIEW Perturbed Particle Swarm Optimization Based Approach for Model Predictive Control Tuning

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IFAC-PapersOnLine 2016年第5期49卷 353-358页

作者： Derouiche, Mohamed Lotfi Bouallègue, Soufiene Haggège, Joseph Sandou, Guillaume National Engineering School of Tunis BP 37 Le Belvédère Tunis1002 Tunisia L2S CentraleSupélec CNRS Université Paris Sud Université Paris-Saclay 3 rue Joliot Curie Gif-sur-Yvette Cedex91192 France

In this paper, a new Model Predictive Controller (MPC) parameters tuning strategy is proposed using a Lab VIEW-based perturbed Particle Swarm Optimization (pPSO) approach. This original LabVIEW implementation of this metaheuristic algorithm is firstly validated on some test functions in order to show its efficiency and validity. The optimization results are compared with the standard PSO approach. The parameters tuning problem, i.e. the weighting factors on the output error and input increments of the MPC algorithm, is then formulated and systematically solved, using the proposed LabVIEW pPSO algorithm. The case of a Magnetic Levitation (MAGLEV) system is investigated to illustrate the robustness and superiority of the proposed pPSO-based tuning MPC approach. All obtained simulation results, as well as the statistical analysis tests for the formulated control problem with and without constraints, are discussed and compared with the Genetic algorithm Optimization (GAO)-based technique in order to improve the effectiveness of the proposed pPSO-based MPC tuning methodology. © 2016

关键词： Particle swarm optimization (PSO) Genetic algorithms Magnetic levitation Magnetic levitation vehicles Model predictive control Parameter estimation Predictive control systems Genetic algorithm optimizations LabViEW Maglev Systems Magnetic levitation (maglev) meta heuristic algorithm Model predictive controllers Parameters tuning Weighting factors

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Electrochemical Model Parameter Estimation of a Lithium-Ion Battery Using a metaheuristic algorithm: Cascaded Improved Harmony Search

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IFAC-PapersOnLine 2018年第28期51卷 409-413页

作者： Chun, Huiyong Han, Soohee Pohang37673 Korea Republic of

For the safe and accurate operation of lithium-ion battery, the estimation of inner states of battery is necessary. Among many models of battery, electrochemical model, especially P2D model, represents the real dynamics such as transport and diffusion phenomena of charges and lithium-ions. For this reason, P2D model is an appropriate model for estimating the states. In this paper, metaheuristic algorithm is applied to estimate these parameter values, since it is more useful to implement the metaheuristic method than other jacobian-based methods, if the number of the parameters to be estimated is increased. Harmony search is one of the metaheuristic algorithms, and improved harmony search was developed for high convergence rate and fine-tunning. The cascaded version of improved harmony search is proposed as the estimation algorithm in this paper, and it is the first time to using the harmony search for lithium-ion battery parameterization. As a result, the parameters had reasonable values compared to reference values and lower voltage, temperature errors compared to the results of improved harmony search which is employed as a benchmark. © 2018

关键词： Lithium-ion batteries Ions Parameter estimation Appropriate models Convergence rates Electrochemical modeling Estimation algorithm Improved harmony searches meta heuristic algorithm meta heuristic methods Transport and diffusions

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