Swarm and Evolutionary Computation最新文献_第5页

Solving nonlinear equation systems and real-world engineering example via adaptive information migration and sharing evolutionary multitasking algorithm with cross-sampling 基于自适应信息迁移和交叉采样共享进化多任务算法求解非线性方程组和工程实例

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-07-01 DOI: 10.1016/j.swevo.2025.102059

Zhihui Fu , Suruo Li

{"title":"Solving nonlinear equation systems and real-world engineering example via adaptive information migration and sharing evolutionary multitasking algorithm with cross-sampling","authors":"Zhihui Fu , Suruo Li","doi":"10.1016/j.swevo.2025.102059","DOIUrl":"10.1016/j.swevo.2025.102059","url":null,"abstract":"<div><div>In practical engineering problems, nonlinear equation systems (NESs) are widely present, such as power systems and control systems. With the increase of system scale and the complexity of problems, solving these NESs becomes increasingly difficult. Although existing methods have proposed effective improvement methods from multiple perspectives, they still ignore the key issue that the implicit relationship between different NESs can promote the evolution of algorithms. Therefore, this paper proposes adaptive cross-sampling evolutionary multitasking algorithm framework, namely AC-MTNESs, to solve NESs. This framework establishes the implicit relationship between tasks through unified encoding method, and proposes an adaptive information migration and sharing selection mechanism, combined with fractional calculus methods, to more accurately capture the nonlinear relationship between NESs. In addition, to ensure that the algorithm maintains the level of population diversity, this work propose a cross-resource sampling strategy, which balances the exploration and exploitation capabilities of the algorithm by archiving roots that meet the accuracy threshold and reusing resources from different distributions to cross-generate offspring. Experiments verify the superiority of the algorithm on 30 standard and 18 complex NESs problem sets. The results show that AC-MTNESs outperforms existing methods. Furthermore, it also shows good application potential in practical problems of motor systems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102059"},"PeriodicalIF":8.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A distributed multi-agent deep reinforcement learning approach for dynamic beam hopping optimization in LEO mega-constellations 基于分布式多智能体深度强化学习的低轨道大星座动态波束跳变优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-30 DOI: 10.1016/j.swevo.2025.102039

Kexin Chen, Xiaolu Liu, Wei Li

{"title":"A distributed multi-agent deep reinforcement learning approach for dynamic beam hopping optimization in LEO mega-constellations","authors":"Kexin Chen, Xiaolu Liu, Wei Li","doi":"10.1016/j.swevo.2025.102039","DOIUrl":"10.1016/j.swevo.2025.102039","url":null,"abstract":"<div><div>With the rapid advancement of low Earth orbit (LEO) satellite networks, conventional static beam allocation methods have become insufficient in addressing the challenges posed by dynamic traffic demands and uneven user distribution. To overcome these limitations, we propose a novel beam hopping scheduling approach specifically designed for dealing with uncertain channel conditions and time-varying traffic requirements in LEO satellite systems. We first develop a multi-objective optimization model that effectively balances the critical performance metrics of throughput and delay in LEO satellite networks. Building upon this foundation, we formulate a locally interacting Markov game model and rigorously prove the existence of at least one Nash equilibrium, thereby establishing a theoretical basis for our approach. To implement this model effectively, we introduce the Multi-Agent Deep Q-Network with Local Cooperative Rewards (MDQN-LCR) algorithm, which enables satellites to make intelligent decisions through a distributed Q-learning framework enhanced by a cooperative reward mechanism. Through extensive simulation experiments in diverse scenarios, results demonstrate that MDQN-LCR outperforms existing centralized methods by achieving 2.6% higher throughput in large-scale deployments and 13.5% lower transmission delay in short time slots. Our approach demonstrates superior stability with a confidence interval 42% smaller than that of centralized QMIX, while significantly reducing communication overhead through its distributed architecture. This makes our solution particularly suitable for large constellation scenarios , thus offering a practical and scalable alternative for next-generation satellite communication systems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102039"},"PeriodicalIF":8.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Convergence analysis for a class of continuous single-step meta-heuristic algorithms based on Harris chain 一类基于Harris链的连续单步元启发式算法的收敛性分析

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-28 DOI: 10.1016/j.swevo.2025.102046

Haoxin Wang, Libao Shi

{"title":"Convergence analysis for a class of continuous single-step meta-heuristic algorithms based on Harris chain","authors":"Haoxin Wang, Libao Shi","doi":"10.1016/j.swevo.2025.102046","DOIUrl":"10.1016/j.swevo.2025.102046","url":null,"abstract":"<div><div>As one of the most important properties to measure the optimization performance of a meta-heuristic algorithm (MA), the convergence property has been widely concerned and studied. So far, most theoretical research in this field has mainly focused on specific MAs, and the corresponding Markov chain theory has also been mainly utilized to analyze the MAs with discrete finite states. How to further investigate the convergence of a class of continuous single-step MAs from the perspective of theoretical analysis still needs in-depth and detailed exploration. In this paper, for a class of single-step MAs, the sampling distribution convergence and global convergence are elaborately analyzed based on Harris chain. Firstly, based on the similarities of the formulation of solution update operator, a class of single-step MAs are defined. Then, the corresponding transition kernel of each search agent position is derived, based on which a sufficient condition for sampling distribution convergence and global convergence is proposed and rigorously proved through Harris chain theory. Finally, some case studies are performed to verify the rationality and effectiveness of the proposed definitions, conditions, and theorems. On the basis of these cases, some meaningful conclusions are drawn to provide guidance for leveraging existing single-step MAs and designing efficient single-step MAs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102046"},"PeriodicalIF":8.2,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144500992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nature-Inspired optimization algorithms for enhanced load balancing in cloud computing: A comprehensive review with taxonomy, comparative analysis, and future trends 云计算中用于增强负载平衡的受自然启发的优化算法：分类、比较分析和未来趋势的全面回顾

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-28 DOI: 10.1016/j.swevo.2025.102053

Farida Siddiqi Prity

引用次数: 0

Data-driven optimization of asymmetric curved winglets in fin-and-tube heat exchangers 翅管式换热器非对称弯曲小翼的数据驱动优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-28 DOI: 10.1016/j.swevo.2025.102056

Rishikesh Sharma, D.P. Mishra, Lakhbir Singh Brar

{"title":"Data-driven optimization of asymmetric curved winglets in fin-and-tube heat exchangers","authors":"Rishikesh Sharma, D.P. Mishra, Lakhbir Singh Brar","doi":"10.1016/j.swevo.2025.102056","DOIUrl":"10.1016/j.swevo.2025.102056","url":null,"abstract":"<div><div>The drive to reduce energy consumption and enhance heat transfer capability makes the optimization of heat exchangers (HEs) crucial in modern thermal management systems. Recognizing the potential for significant energy savings, this study focuses on the novel asymmetrically placed curved winglets in fin-and-tube heat exchangers. A total of 2320 simulations, designed using a Latin hypercube sampling plan, have been performed. The dependent variables are calculated using computational fluid dynamics to train artificial neural networks that serve as a surrogate model for genetic algorithm (GA) to perform multi-objective optimization. The GA aims to maximize the enhancement factor (<em>η</em>) – a ratio of the Colburn and friction factors.</div><div>Since HEs operate over a range of Reynolds numbers (<em>Re</em>), all the previous optimization-based studies have been based on a single <em>Re</em> that raises several questions about the HE’s performance at off-design conditions (over a range of <em>Re</em>). Hence, the present optimization-based study considers three different (but fixed) <em>Re</em> values, followed by optimizing <em>Re</em> against each optimized winglet geometry. All the results are compared at design and off-design conditions. The cross-validation of the Pareto front points using CFD reveals a deviation of <5 %, indicating good predictive performance and consistency of the optimized datasets within the defined simulation framework. Compared to the baseline model without winglets, the optimized designs achieved <em>η<sub>max</sub></em> = 100.17 % and also outperformed under varied operating conditions. Hence, besides introducing a novel asymmetric design, this research provides guidelines on <em>Re</em>-based optimizations that could significantly improve HE performance in energy-dependent industries.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102056"},"PeriodicalIF":8.2,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144500991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The scheduling problem with delivery and observation in collaboration after the disaster 灾后协同交付和观察的调度问题

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-27 DOI: 10.1016/j.swevo.2025.102047

Li Chen , Enming Chen , Ruiyang Li , Zhongbao Zhou , Wenting Sun , Jianmai Shi

{"title":"The scheduling problem with delivery and observation in collaboration after the disaster","authors":"Li Chen , Enming Chen , Ruiyang Li , Zhongbao Zhou , Wenting Sun , Jianmai Shi","doi":"10.1016/j.swevo.2025.102047","DOIUrl":"10.1016/j.swevo.2025.102047","url":null,"abstract":"<div><div>After the disaster, incremental disaster points, uncertain information on emergency needs, and changing weather impede emergency deliveries and increase the risk to delivery personnel. This paper introduces the scheduling problem with a delivery vehicle and a high-performance observation unmanned aerial vehicle (UAV) in collaboration (SP-DVOUC) to address the difficulties encountered in emergency delivery. In the SP-DVOUC, the routes of the UAV and the delivery vehicle depend on the information updates on points, needs, and changing weather. Considering the information updates, we solve the SP-DVOUC by the rolling-horizon approach. We formulate a MIP model and propose two acceleration strategies specific to post-disaster dynamics, an insertion heuristic designed to rapidly restore solution feasibility during dynamic updates, and a large neighborhood search algorithm for the fast rolling-horizon approach. After extensive experiments based on the Yushu Earthquake in China, the performance of the SP-DVOUC and the fast rolling-horizon approach is verified. In particular, our algorithm outperforms three algorithms used in other studies to solve similar problems. In addition, some suggestions for urgent delivery and algorithm parameterization are given.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102047"},"PeriodicalIF":8.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal placement of phasor measurement unit for implementation of WAMS in a grid system 在电网系统中实现WAMS的相量测量单元的优化布置

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-27 DOI: 10.1016/j.swevo.2025.102041

Yuvaraju Venkatachalam , Thangavel Subbaiyan , Mallikarjuna Golla

{"title":"Optimal placement of phasor measurement unit for implementation of WAMS in a grid system","authors":"Yuvaraju Venkatachalam , Thangavel Subbaiyan , Mallikarjuna Golla","doi":"10.1016/j.swevo.2025.102041","DOIUrl":"10.1016/j.swevo.2025.102041","url":null,"abstract":"<div><div>The effective operation of power systems relies on real-time monitoring and control to ensure stability and reliability through rapid fault detection and service restoration. The supervisory control and data acquisition system-based monitoring play a significant role in grid supervision, but it lacks the time-synchronized phasor measurements needed for dynamic grid analysis. In this context, phasor measurement units (PMUs) play a vital role, providing high-resolution, synchronized data for more accurate and effective grid monitoring. However, the bulk volume of PMU data leads to traffic in data transmission that causes a delay in data reception at the control center. This, in turn, affects the effectiveness of the protection system. In this regard, a data traffic model is introduced in a wide-area monitoring system (WAMS) to estimate the data traffic index (DTI), reducing the number of PMUs installed to minimize system cost. This paper proposes the application of the teaching learning-based optimization (TLBO) algorithm for solving optimal PMU placement (OPP) problems that considers the WAMS DTI and installation cost index. In addition, the zero injection bus case is considered to reduce the number of PMUs further, thereby reducing installation costs. The TLBO algorithm is tested on the Indian utility grid 62-bus system, 49-bus system, 83-bus system in Tamil Nadu, and 31-bus system in Kerala state. The simulation results demonstrate the efficacy of the algorithm in solving OPP problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102041"},"PeriodicalIF":8.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A knowledge-guided evolutionary algorithm incorporating reinforcement learning for energy efficient dynamic flexible job shop scheduling problem with machine breakdowns 基于强化学习的知识导向进化算法求解机器故障下的高效动态柔性作业车间调度问题

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-27 DOI: 10.1016/j.swevo.2025.102050

Zhixiao Li , Guohui Zhang , Nana Yu , Shenghui Guo , Wenqiang Zhang

{"title":"A knowledge-guided evolutionary algorithm incorporating reinforcement learning for energy efficient dynamic flexible job shop scheduling problem with machine breakdowns","authors":"Zhixiao Li , Guohui Zhang , Nana Yu , Shenghui Guo , Wenqiang Zhang","doi":"10.1016/j.swevo.2025.102050","DOIUrl":"10.1016/j.swevo.2025.102050","url":null,"abstract":"<div><div>The flexible job shop scheduling problem is gradually developing towards greening and intelligence. However, in the real production, there are often various dynamic disturbances that result in lower executability of scheduling solutions. Therefore, this paper first investigates the energy efficient dynamic flexible job shop scheduling problem with machine breakdowns. To solve this problem, a knowledge-guided evolutionary algorithm incorporating reinforcement learning (KEARL) is established to minimize maximum completion time, total energy consumption, and workload of critical machines, which is a mixed-integer linear programming model with transportation time of jobs and setup time of machines included. In KEARL, a new rescheduling strategy is designed to reduce the possibility of the machine's second breakdown. In addition, four knowledge-guided initialization methods are also designed and a reinforcement learning-based parameter adaptive strategy is used to optimize the crossover probability and mutation probability, while a knowledge-guided variable neighborhood search strategy enhances the search capability of KEARL. More importantly, three energy efficient methods are implemented to reduce the energy consumption of the production process. Finally, through extensive experiments, the KEARL is compared with several well-known algorithms. The experimental results indicate that KEARL outperforms the other algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102050"},"PeriodicalIF":8.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An augmented variable neighborhood search for mixed-model two-sided assembly line balancing considering PM scenarios 考虑PM场景的混合模型双面装配线平衡增广变量邻域搜索

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-26 DOI: 10.1016/j.swevo.2025.102043

Lianpeng Zhao , Qiuhua Tang

{"title":"An augmented variable neighborhood search for mixed-model two-sided assembly line balancing considering PM scenarios","authors":"Lianpeng Zhao , Qiuhua Tang","doi":"10.1016/j.swevo.2025.102043","DOIUrl":"10.1016/j.swevo.2025.102043","url":null,"abstract":"<div><div>In a real mixed-model two-sided assembly line, preventive maintenance (PM) activities cause capacity waste at available stations and production halts. To mitigate these issues, multiple task assignment schemes with high interchangeability are required, each tailored to one specific scenario. However, the resulting mixed-model two-sided assembly line balancing problem considering PM scenarios (MTALBP-PM) has not been studied. Therefore, a mixed-integer linear programming model is formulated to minimize total cycle time and task adjustment simultaneously. Meanwhile, driven by knowledge and learning, an augmented variable neighborhood search (AVNS) is designed. Concretely, with the guidance of problem-specific knowledge, a decoding mechanism and three objective-oriented neighborhood structures are designed to achieve solutions with better objectives. Using unsupervised learning, an initialization heuristic is mined from tacit information to obtain high-quality initial solutions. With historical search information, a self-adaptive strategy based on Q-learning is proposed to recommend the best-fit neighborhood structure for higher efficiency. Besides, an auto-tuning restart operator based on multi-domain knowledge is employed to escape local optima. Experimental results show that the espoused policy is effective, and AVNS outperforms eight other state-of-the-art meta-heuristics in deriving well-converged and -distributed Pareto fronts. In a statistical sense, the average <em>GD, IGD</em>, and <em>HVR</em> of AVNS reach the best values among all tested meta-heuristics based on 40 benchmark cases, which are 0.4599, 0.8021, and 0.8943, respectively.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102043"},"PeriodicalIF":8.2,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regularity-based evolutionary multi-objective optimization review 基于规则的进化多目标优化综述

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-06-25 DOI: 10.1016/j.swevo.2025.101999

Yulan Lu , Xueyi Guo , Jinggai Geng , Shuai Wang , Zhenkun Wang , Aimin Zhou , Jianyong Sun , Xinhui Si , Xin Sun , Hu Zhang

引用次数: 0