Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

Bai, Hui; Fan, Tian; Ni, Yuan; Cui, Zhihua

doi:10.23919/csms.2022.0006

Cited by 17 publications

(10 citation statements)

References 35 publications

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“…The development of FL has experienced three stages: traditional privacy protection, FL and security FL. Basically, all kinds of commonly used machine learning algorithms can adopt FL method for model training, support structured, text, image and other types of data sources, and can be applied in sample classification, path programming [164], regression prediction, image recognition [165,166], gene analysis, natural language and other tasks. In recent years, FL has played an important role in health care, finance, Internet of things, urban services and other fields where there is a strictly requirement for privacy protection.…”

Section: Discussionmentioning

confidence: 99%

A survey on federated learning: challenges and applications

Zhang

Lan

et al. 2022

Int. J. Mach. Learn. & Cyber.

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y impact on human life [1][2][3]. As great progress has been made in AI technology in recent years, various application fields have stepped into intelligence [4,5]. On the road of AI development, models, computing power, chip performance, and other technical issues have been the focus of academic research, so that AI technology can continue to evolve. For machines to truly approach the level of human thought, they need to be trained with vast amounts of real data [6,7]. However, cloud computing power, data security, data silos and other risks will inevitably become constraints for AI to win user trust, collect private data, and achieve largescale implementation [8]. Therefore, it is an urgent need for a practical and effective technique to alleviate the above problems and make the AI full of vitality again. Under this background, the concept of "federated learning (FL) " came into being.The notion of FL is first proposed by Google in 2016, mainly to make android mobile phone users update models locally without revealing private personal data [9]. After then, Google implemented an application-oriented FL system. The designed FL system, which focused on running federated average (FedAvg) algorithms on mobile phones, can perform federated analytics and be applied to monitor statistics for large-scale cluster equipment without recording raw device data to the cloud server. FL is one of the most Zhihua Cui

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Section: Discussionmentioning

confidence: 99%

A survey on federated learning: challenges and applications

Zhang

Lan

et al. 2022

Int. J. Mach. Learn. & Cyber.

Self Cite

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“…Deb proposed a fast, non-dominated-sorting genetic algorithm based on reference points (NSGA-III) [11], which replaced the congestion by associating the reference points, and solved the problem of high-dimensional objective optimization. Many scholars have solved high-dimensional multi-objective optimization problems based on the above algorithms, including the multi-objective classification problem [12], reservoir flood-control-operation problem [13], resource-allocation problem [14][15][16], location-routing problem [17][18][19][20], high-dimensional target power-flow-optimization problem of a power system [21,22], etc.…”

Section: Principle Of I-nsga-iii-vlc Methodsmentioning

confidence: 99%

Modeling and I-NSGA-III-VLC Solution of Aircraft Equipment Rotation and Echelon Usage under Uncertainty

Miao¹,

Zhang²,

Qin³

et al. 2022

Applied Sciences

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Optimizing the aircraft equipment usage scheme of different units according to their task intensity has great significance in improving aircraft reliability and health management. This paper studied the modeling and solving methods of the rotation and echelon usage problems of aircraft equipment measured by dual-life indexes, one of which cannot be controlled. In order to maximize the waste rate of the rotation quantity, echelon uniformity index, life matching index and life utilization index, a decision-making model of the equipment rotation and echelon usage problem under uncertainty was constructed, and an improved NSGA-III with a variable length chromosome was proposed. An improved segmented coding method and operators were proposed, and the repeated individual control mechanism was used to improve the population diversity. When the scale of the problem was large, this method could search a wider range in a short time and obtain more feasible solutions, which verified the feasibility of this method.

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“…Li et al [28] set up a mathematical model and proposed an improved harmonic search algorithm with the objectives of AGV traveling distance, standard deviation of loading capacity, and standard deviation of the difference between the latest and expected delivery times. Eda et al [29] established a model with AGV travel and balanced delivery times as targets, and proposed a Petri net decomposition method. Bai et al [30] established a model aiming at the trajectory distance, trajectory time, trajectory threat, and trajectory coordination distance cost of UAVs, and used the NSGA-III algorithm to solve the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Kang,

Miao,

Pan

et al. 2024

Tsinghua Sci. Technol.

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With the emergence of the artificial intelligence era, all kinds of robots are traditionally used in agricultural production. However, studies concerning the robot task assignment problem in the agriculture field, which is closely related to the cost and efficiency of a smart farm, are limited. Therefore, a Multi-Weeding Robot Task Assignment (MWRTA) problem is addressed in this paper to minimize the maximum completion time and residual herbicide. A mathematical model is set up, and a Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm is presented to solve the problem. In the MOTLBO algorithm, a heuristicbased initialization comprising an improved Nawaz Enscore, and Ham (NEH) heuristic and maximum loadbased heuristic is used to generate an initial population with a high level of quality and diversity. An effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule. A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the algorithm. Finally, a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the literature. Experimental results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.

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Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

Cited by 17 publications

References 35 publications

A survey on federated learning: challenges and applications

A survey on federated learning: challenges and applications

Modeling and I-NSGA-III-VLC Solution of Aircraft Equipment Rotation and Echelon Usage under Uncertainty

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

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