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multi-objective evolutionary algorithm with two balancing mechanisms for heterogeneous UAV swarm path planning

APPLIED SOFT COMPUTING 2025年 173卷

作者： Xu, Xiuju Xie, Chengyu Ma, Linru Yang, Lin Zhang, Tao Sun Yat Sen Univ Sch Syst Sci & Engn Guangzhou 510275 Peoples R China Acad Mil Sci Inst Syst Engn Natl Key Lab Sci & Technol Informat Syst Secur Beijing 100141 Peoples R China Acad Mil Sci Inst Syst Engn Beijing 100141 Peoples R China

Unmanned aerial vehicle (UAV) swarm path planning involves creating efficient routes based on task requirements to enable collaborative flight. Compared to homogeneous UAV swarm, the application scenarios of heterogeneous UAV swarm have become increasingly widespread. They can fully leverage the various capabilities of drones and show higher economic benefits. Existing research mainly focuses on homogeneous UAV swarms, and the model for uniformly describing heterogeneous UAV swarm from a functional perspective is insufficient. Differences in dynamic constraints and energy consumption models create challenges for accurately characterizing the path planning problem of heterogeneous UAV swarm. To supplement the above deficiencies, this article designs the scenario and composition structure of heterogeneous UAV swarm. The path-planning problem of heterogeneous UAV swarm is modeled as a multi-objective optimization (MOO) problem, in which a comprehensive energy consumption objective is constructed. To better balance multiple objectives and obtain high-quality solutions, a MOO evolutionary algorithm based on heterogeneous UAV swarm, namely HMOEA, is proposed. Specifically, HMOEA is implemented by combining the proposed two strategies. To verify the model's feasibility and the algorithm's effectiveness, numerical simulations and prototype simulations are provided. In numerical simulations, the proposed algorithm was compared with various advanced algorithms, i.e., NSGA-II, CIACO, AP-GWO, CL-DMSPSO, and DSNSGA-III, in two designed terrain problems. The results demonstrate that HMOEA not only outperforms the compared algorithms on convergence and diversity indicators increased over 4% and 2% respectively. Normal flight results were achieved in the two scenarios served by the prototype simulation, namely, urban buildings and forest scenes. Specific implementation and application can be achieved in military or civilian scenarios like reconnaissance and strike missions, search

关键词： UAV swarm Path planning Heterogeneous UAV swarm multi-objective evolutionary algorithm

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multi-objective evolutionary algorithm for vehicle routing problem with time window under uncertainty

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evolutionary INTELLIGENCE 2023年第2期16卷 493-508页

作者： Tan, Fei Chai, Zheng-yi Li, Ya-lun Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

Vehicles route problems (VRP) are to arrange the optimal routes under the various requirements, and it is becoming significant in the logistics industry as electric commerce is rising. However, uncertainty is inevitable in VRP. In this manuscript, we consider the VRP with time windows (VRPTW) under uncertainty. We formulate the robust multi-objective VRPTW (RMOVRPTW) model and propose a robust optimization algorithm based on MOEA/D (R-MOEAD-VRP) for simultaneously optimizing the total distance and the number of vehicles required for transport. First, we use the priority of the customers being served to encode and use the defined transformation approach to form the feasible routes. Next, we employ Order Crossover and Exchange mutation operators to increase population diversity. For the new routes by reproduction, we use Monte-Carlo tests to check the feasibility of the routes after adding uncertainty. For the feasible routes, we calculate the solution robustness values based on the defined method. Finally, we consider both optimality and robustness to form a set of highly robust and relatively optimal solutions. For verifying the availability of the presented algorithm, the simulation experiments conduct on Solomon's benchmark problems compared with several related algorithms. Experimental results show that our proposed algorithm can bring more robust and non-dominated solutions under uncertainty and can achieve good performance.

关键词： Vehicle routing problem with time window (VRPTW) multi-objective evolutionary algorithm Robust optimization MOEA D

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multi-objective evolutionary algorithm on reliability redundancy allocation with interval alternatives for system parameters

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NEURAL COMPUTING & APPLICATIONS 2022年第21期34卷 18595-18609页

作者： Maneckshaw, B. Mahapatra, G. S. Natl Inst Technol Puducherry Dept Math Karaikal 609609 India

This paper presents a multi-objective reliability redundancy allocation model with constraints representing the system complexity. A reliability model to enhance the system reliability and to diminish the system cost through a feasible redundancy in its stages is proposed. We develop an evolutionary algorithm using precedence and dominance property to obtain Pareto-optimal solutions guided by a Euclidean norm. An illustration of the model is presented for a series-parallel configuration of an oil transportation subsystem. The results are analyzed for various interval alternatives with randomization in the interval parameter.

关键词： Reliability Redundancy multi-objective evolutionary algorithm Pareto front Max-min dominance

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A dual dynamic constraint boundary based constrained multi-objective evolutionary algorithm for small feasible regions

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 275卷

作者： Zhu, Cong Yang, Yongkuan Kong, Xiangsong Yang, Yanxiang Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Xiamen Key Lab Frontier Elect Power Equipment & In Xiamen 361024 Peoples R China

Addressing constrained multi-objective optimization problems (CMOPs) with small feasible regions presents a significant challenge, as existing algorithms often struggle to balance feasibility, diversity, and convergence within the population. To overcome this challenge, we propose a dual dynamic constraint boundary-based constrained multi-objective evolutionary algorithm, referred to as TPDCB. In TPDCB, the original CMOP is transformed into two dynamic CMOPs using a dual dynamic constraint boundary strategy to better identify feasible solutions. Specifically, for the two dynamic CMOPs within the constraint relaxation boundary, the first dynamic CMOP primarily focuses on multi-objective optimization, while the second dynamic CMOP equally emphasizes both multi-objective optimization and constraint satisfaction to enhance individual diversity. Furthermore, an auxiliary problem without constraints is introduced by treating constraint violations as an additional optimization objective, which improves the algorithm's global convergence. Finally, a tri-population co-evolution framework is proposed to simultaneously tackle all three constructed problems. The algorithm's performance is evaluated on 22 benchmark problems and three real-world applications, and compared to seven state-of-the-art algorithms. Experimental results demonstrate that TPDCB is competitive in solving CMOPs with small feasible regions.

关键词： Constrained multi-objective optimization multi-objective evolutionary algorithm Small feasible region Dynamic constraint boundary Constraint handling Co-evolution

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APPLICATION OF IMPROVED multi-objective evolutionary algorithm IN INTELLIGENT TOURISM INTEREST POINT RECOMMENDATION AND ITINERARY PLANNING

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MECHATRONIC SYSTEMS AND CONTROL 2025年第2期53卷 126-133页

作者： Du, Jinling Tourism Coll Zhejiang Sch Hospitality Management Hangzhou 311231 Peoples R China

The rapid development of smart tourism has brought new opportunities and challenges to people's travel experience. However, due to numerous interest points and complex and diverse needs of tourists, traditional methods often cannot meet the needs of individual and multi-objective. To solve this problem, this paper proposes to establish an evaluation model which takes into account various factors, such as tourists' interest preference, time constraint, and budget constraint. By introducing an improved evolutionary algorithm, genetic operators are combined with local search strategies to generate personalised travel routes. Experimental results show that the improved multi-objective evolutionary algorithm performs well in point of interest recommendation and trip planning. Compared with traditional methods, WAEA algorithm can better meet the individual needs of tourists and deal with multi-objective optimisation problems.

关键词： multi-objective evolutionary algorithm smart tourism recommen- dation of interest points journey planning

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A novel multi-state reinforcement learning-based multi-objective evolutionary algorithm

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INFORMATION SCIENCES 2025年 688卷

作者： Wang, Jing Zheng, Yuxin Zhang, Ziyun Peng, Hu Wang, Hui Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330032 Peoples R China Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang Peoples R China

multi-objective evolutionary algorithms (MOEAs) are widely employed to tackle multi-objective optimization problems (MOPs). However, the choice of different crossover operators significantly impacts the algorithm's ability to balance population diversity and convergence effectively. To enhance algorithm performance, this paper introduces a novel multi-state reinforcement learning- based multi-objective evolutionary algorithm, MRL-MOEA, which utilizes reinforcement learning (RL) to select crossover operators. In MRL-MOEA, a state model is established according to the distribution of individuals in the objective space, and different crossover operators are designed for the transition between different states. Additionally, in the process of evolution, the population still exhibits inadequate convergence in certain regions, leading to sparse areas within the regular Pareto Front (PF). To address this issue, a strategy for adjusting weight vectors has been devised to achieve uniform distribution of the PF. The experimental results of MRL-MOEA on several benchmark suites with a varying number of objectives ranging from 3 to 10, including WFG and DTLZ, demonstrate MRL-MOEA's competitiveness compared to other algorithms.

关键词： multi-objective evolutionary algorithm Decomposition Reinforcement learning Matching mechanism multiple strategies

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A multi-objective evolutionary algorithm for solving the feature selection problem of high-dimensional sparse data and its application in the absorption, distribution, metabolism, excretion and toxicity (ADMET) classification

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ENGINEERING OPTIMIZATION 2025年

作者： Liu, Yu Wang, Jie-Sheng Wen, Jia-Yao Li, Yu-Tong Yan, Peng-Guo Univ Sci & Technol Sch Elect & Informat Engn Anshan Liaoning Peoples R China

A novel multi-objective sparse evolutionary algorithm (MOSEA) is proposed to solve the feature selection (FS) problem in high-dimensional sparse data in the medical field, which aims to balance the accuracy of ADMET classification with the number of features used during the drug design phase. Based on the architecture of evolutionary algorithms, MOSEA enhances search efficiency and convergence speed through an initial scoring strategy. It binarizes the real-valued vectors of the decision space using an X-shaped transfer function, applies a dynamic scoring strategy for sparsification, merges offspring and parent populations and uses an angular function for environmental selection in fitness evaluation, thereby optimizing the FS process. Compared with six multi-objective optimization algorithms, MOSEA has significant advantages in terms of the performance of the FS problem for high-dimensional sparse datasets and hepatotoxicity datasets, indicating that it can effectively solve the multi-objective FS problem of ADMET hepatotoxicity.

关键词： High-dimensional sparseness multi-objective evolutionary algorithm feature selection angle function ADMET

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A Q-learning driven multi-objective evolutionary algorithm for worker fatigue dual-resource-constrained distributed hybrid flow shop

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COMPUTERS & OPERATIONS RESEARCH 2025年 175卷

作者： Song, Haonan Li, Junqing Du, Zhaosheng Yu, Xin Xu, Ying Zheng, Zhixin Li, Jiake Liaocheng Univ Sch Comp Liaocheng 252000 Shandong Peoples R China Yunnan Normal Univ Dept Math Kunming 650500 Yunnan Peoples R China HengXing Univ Sch Informat Engn Qingdao 266199 Peoples R China

In practical industrial production, workers are often critical resources in manufacturing systems. However, few studies have considered the level of worker fatigue when assigning resources and arranging tasks, which has a negative impact on productivity. To fill this gap, the distributed hybrid flow shop scheduling problem with dualresource constraints considering worker fatigue (DHFSPW) is introduced in this study. Due to the complexity and diversity of distributed manufacturing and multi-objective, a Q-learning driven multi-objective evolutionary algorithm (QMOEA) is proposed to optimize both the makespan and total energy consumption of the DHFSPW at the same time. In QMOEA, solutions are represented by a four-dimensional vector, and a decoding heuristic that accounts for real-time worker productivity is proposed. Additionally, three problem-specific initialization heuristics are developed to enhance convergence and diversity capabilities. Moreover, encoding-based crossover, mirror crossover and balanced mutation methods are presented to improve the algorithm's exploitation capabilities. Furthermore, a Q-learning based local search is employed to explore promising nondominated solutions across different dimensions. Finally, the QMOEA is assessed using a set of randomly generated instances, and a detailed comparison with state-of-the-art algorithms is performed to demonstrate its efficiency and robustness.

关键词： Distributed hybrid flow shop scheduling Dual-resource constraints Worker fatigue Q-learning multi-objective evolutionary algorithm

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Optimal placement of fixed hub height wind turbines in a wind farm using twin archive guided decomposition based multi-objective evolutionary algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 130卷

作者： Raju, Sri Srinivasa M. Mohapatra, Prabhujit Dutta, Saykat Mallipeddi, Rammohan Das, Kedar Nath Natl Inst Technol Dept Math Silchar India Vellore Inst Technol Sch Adv Sci Dept Math Vellore Tamil Nadu India Acad Sinica Inst Stat Sci Taipei Taiwan Kyungpook Natl Univ Sch Elect Engn Dept Artificial Intelligence Daegu 41566 South Korea

Harnessing maximum wind energy's power output and efficiency is vital to combat environmental challenges tied to conventional fossil fuels. Wind power's cost-effectiveness and emission reduction potential underscore its significance. Efficient wind farm layout plays a pivotal role, both technically and commercially. evolutionary algorithms show their potential while solving multi-objective wind farm layout optimization problems. However, due to the large-scale nature of the problems, existing algorithms are getting trapped into local optima and fail to explore the search space. To address this, the TAG-DMOEA algorithm is upgraded with an adaptive offspring strategy (AOG) for better exploration. The proposed algorithm is employed on a wind farm layout problem with real-time data of wind speed and direction from two different locations. Unlike mixed hub heights, fixed hub heights such as 60, 67, and 78 m are adopted to conduct the case studies at two potential locations with real-time statistical data for the investigation of improved results. The results obtained by TAG-DMOEA-AOG on six cases are compared with 10 state-of-the-art algorithms. Statistical tests such as Friedman test and Wilcoxon signed rank test along with post hoc analysis (Nemenyi test) confirmed the superiority of the TAG-DMOEA-AOG on all cases of the considered multi-objective wind farm layout optimization problem.

关键词： Wind turbine multi-objective evolutionary algorithm Optimization Decomposition Weight vector

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Transfer Learning Based multi-objective evolutionary algorithm for Dynamic Workflow Scheduling in the Cloud

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2024年第4期12卷 1200-1217页

作者： Xie, Huamao Ding, Ding Zhao, Lihong Kang, Kaixuan Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China Minist Educ Key Lab Big Data & Artificial Intelligence Transpo Beijing 100044 Peoples R China

Managing scientific applications in the Cloud poses many challenges in terms of workflow scheduling, especially in handling multi-objective workflow scheduling under quality of service (QoS) constraints. However, most studies address the workflow scheduling problem on the premise of the unchanged environment, without considering the high dynamics of the Cloud. In this paper, we model the constrained workflow scheduling in a dynamic Cloud environment as a dynamic multi-objective optimization problem with preferences, and propose a transfer learning based multi-objective evolutionary algorithm (TL-MOEA) to tackle the workflow scheduling problem of dynamic nature. Specifically, an elite-led transfer learning strategy is proposed to explore effective parameter adaptation for the MOEA by transferring helpful knowledge from elite solutions in the past environment to accelerate the optimization process. In addition, a multi-space diversity learning strategy is developed to maintain the diversity of the population. To satisfy various QoS constraints of workflow scheduling, a preference-based selection strategy is further designed to enable promising solutions for each iteration. Extensive experiments on five well-known scientific workflows demonstrate that TL-MOEA can achieve highly competitive performance compared to several state-of-art algorithms, and can obtain triple win solutions with optimization objectives of minimizing makespan, cost and energy consumption for dynamic workflow scheduling with user-defined constraints.

关键词： Dynamic scheduling Cloud computing Processor scheduling Heuristic algorithms Quality of service Costs Optimization Dynamic workflow scheduling multi-objective evolutionary algorithm transfer learning quality of service constraints preference optimization

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