Swarm and Evolutionary Computation最新文献

{"title":"An interpretable interval-valued wind power prediction system based on multi-objective feature extraction and base model selection with dynamic ensemble","authors":"Jujie Wang,&nbsp;Yuxuan Lu,&nbsp;Qian Li","doi":"10.1016/j.swevo.2025.101977","DOIUrl":"10.1016/j.swevo.2025.101977","url":null,"abstract":"<div><div>Wind power forecasting is essential for resource optimization and sustainable development. However, current forecasting methods mainly rely on single-valued data with limited information, and the black-box nature of artificial intelligence models weakens the interpretability of the prediction results. This paper introduces a new interpretable model for interval-valued wind power forecasting, which enhances prediction accuracy and reliability by leveraging a feature extraction process, a base model selection strategy, and a dynamic ensemble mechanism. First, to address the complexity of interval-valued wind power data, an interpretable multi-objective feature extraction method is designed to distill key trend and fluctuation features, facilitating in-depth learning of features and their relationships. Considering the alignment between features and models, the contribution of each base model to the prediction target is quantified by combining elastic net regression and Shapley additive explanation method, so as to select the base models under different feature sequences in a highly interpretable way. Finally, the base model weights are dynamically adjusted according to the Shapley values to adapt to the time-varying characteristics of individual model accuracy and realize the online update prediction. An empirical study shows that the suggested model outperforms the benchmark model, demonstrating excellent prediction performance and interpretability.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101977"},"PeriodicalIF":8.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923143","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}

{"title":"Including problem-knowledge based modification into a Differential Evolution Algorithm for optimizing planar moment-resisting steel frames","authors":"Oscar Contreras-Bejarano ,&nbsp;Jesús Daniel Villalba-Morales ,&nbsp;Diego Lopez-Garcia","doi":"10.1016/j.swevo.2025.101958","DOIUrl":"10.1016/j.swevo.2025.101958","url":null,"abstract":"<div><div>The Differential Evolution Algorithm (DEA) has been demonstrated to be capable of effectively addressing engineering challenges, although its performance varies considerably when applied to different problems. Customizing the algorithm to the specific characteristics of a given problem has been identified as a valid strategy to enhance its effectiveness and reliability. In this study, a tailored version of the DEA is proposed for the optimization of planar Moment-Resisting Steel Frames (MRSFs) subjected to static loads. A diverse set of heuristics and techniques were incorporated, including advanced strategies for parameter control, initialization, mutation operators, crossover operators, diversity conservation, constraints handling, and dynamic population management. To evaluate the performance of the proposed heuristics and techniques, 7800 DEA configurations were applied to the optimization of seven representative MRSFs. Results indicate that through problem-specific modifications the DEA is highly likely to identify the optimal solutions. By emphasizing both computational efficiency and solution quality, this research provides valuable insights into enhanced applicability of the DEA to structural optimization problems. It is shown that a customized algorithm is a reliable, effective, and robust tool to optimize MRSFs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101958"},"PeriodicalIF":8.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923144","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}

{"title":"Revolutionizing Wireless Rechargeable Sensor Networks: Speed Optimization-based Charging Scheduling Scheme (SOCSS) for efficient multi-node energy transfer","authors":"Riya Goyal,&nbsp;Abhinav Tomar","doi":"10.1016/j.swevo.2025.101961","DOIUrl":"10.1016/j.swevo.2025.101961","url":null,"abstract":"<div><div>Benefiting from the breakthrough of Wireless Energy Transfer (WET) technology, scheduling multiple Mobile Chargers (MCs) to charge sensor nodes can significantly prolong the lifetime of Wireless Rechargeable Sensor Networks (WRSNs). While previous studies have primarily focused on on-demand recharging within WRSNs, more consideration must be given to utilizing multi-node energy transfer with optimal charging locations to devise efficient charging schedules for requesting sensor nodes. Moreover, existing approaches assume a constant travel speed for MCs and utilize omnidirectional WET, leading to increased energy consumption for MCs and consequently affecting overall charging efficiency. To address these challenges, we propose a novel Speed Optimization-based Charging Scheduling Scheme (SOCSS) for multiple MCs in WRSNs. The initial phase of SOCSS involves clique-based network partitioning to identify minimum cliques and determine optimal charging locations to perform efficient multi-node energy transfer for sensor nodes. The subsequent phase encompasses scheduling and path planning, where the charging schedule is established using efficient Quantum-inspired Particle Swarm Optimization. By integrating speed optimization with the charging schedule, the energy consumption of the MCs is minimized, leading to cost-effective planning of the charging path for energy-constrained MCs. Extensive simulations are conducted to showcase the supremacy of SOCSS across a range of network parameters compared to prior art. In particular, SOCSS has achieved an impressive average reduction of 36.2% in the number of stopping points for MCs, a remarkable 38.9% decrease in the total travel distance, and a 15.7% reduction in the charging delay.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101961"},"PeriodicalIF":8.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923142","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}

{"title":"A matheuristic-based self-learning evolutionary algorithm for lot streaming hybrid flow shop group scheduling with limited auxiliary modules","authors":"Hongxia Tan ,&nbsp;Min Zhou ,&nbsp;Liping Zhang ,&nbsp;Zikai Zhang ,&nbsp;Yingli Li ,&nbsp;Zixiang Li","doi":"10.1016/j.swevo.2025.101965","DOIUrl":"10.1016/j.swevo.2025.101965","url":null,"abstract":"<div><div>Group scheduling enhances production flexibility and efficiency in mass customization However, it overlooks differences of due dates in customized orders and functional/quantity constraints of molds. Therefore, lot streaming and module assignment strategies are needed. To address this, this paper investigates the lot streaming hybrid flow shop group scheduling with limited auxiliary module constraints(HFGSP_LSAM). To minimize the total weighted tardiness and makespan, a new mixed integer linear programming model and a matheuristic-based self-learning evolutionary algorithm(MSEA) are proposed. This algorithm develops a new matheuristic-based hybrid initialization to generate better initial solutions. A double layer self-learning evolution is developed to collaborate operators which include six knowledge-based local search operators and six global crossover operators. The experimental study, based on 360 small and 960 large instances, demonstrates that the matheuristic-based hybrid initialization and double layer self-learning evolution can enhance 84% and 13% performance of MSEA, as well as the proposed MSEA is superior to other well known algorithms in solving HFGSP_LSAM. An industrial case study is conducted to confirm the superiority of MSEA and provide two recommendations for managers to balance production efficiency and due dates.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101965"},"PeriodicalIF":8.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918417","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}

{"title":"Multimodal multi-objective optimization via multi-operator adaptation and clustering-based environmental selection","authors":"Xinyi Wu ,&nbsp;Fei Ming ,&nbsp;Wenyin Gong ,&nbsp;Bolin Liao ,&nbsp;Yuanyuan Guo","doi":"10.1016/j.swevo.2025.101962","DOIUrl":"10.1016/j.swevo.2025.101962","url":null,"abstract":"<div><div>In real world applications, multimodal multi-objective optimization problems are common, addressing which can offer decision makers multiple choices to accommodate varying scenarios. Many researchers have been focusing on this kind of problem, leading to the development of numerous multimodal multi-objective evolutionary optimization algorithms (MMOEAs). However, most existing MMOEAs employ a fixed operator to generate offspring. For different types of problems, the use of hybrid operators can take advantage of their distinct features in reproduction to produce more valuable individuals. To address this issue, we propose an innovative algorithm that integrates two operators collaboratively and dynamically adjusts the proportion of offspring generated by each operator based on its performance throughout the evolution process evaluated by the survival rate. In addition, to better balance the diversity, the proposed algorithm devises a novel clustering method, which clusters the population in the decision space. Then, individuals within the same cluster with better performance in the objective space are able to survive. We evaluate our algorithm against seven representative MMOEAs on two widely used benchmark problems and real-world problems. The experimental results confirm the superior performance and robustness of our approach on both benchmark and real-world problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101962"},"PeriodicalIF":8.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905831","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}

{"title":"Learning-driven memetic algorithm for solving integrated distributed production and transportation scheduling problem","authors":"Shicun Zhao,&nbsp;Hong Zhou","doi":"10.1016/j.swevo.2025.101945","DOIUrl":"10.1016/j.swevo.2025.101945","url":null,"abstract":"<div><div>Production and transportation scheduling are critical components in modern manufacturing. However, existing studies on their integrated optimization are still limited, and most of them focus on the integration of production and local logistics within the shop. Different from previous investigations, this paper considers the integration of production scheduling with transportation across enterprises, which is especially typical and significant for production management in the large-scale distributed manufacturing environment. Considering the energy-aware orientation and production performance, the problem is formulated as a bi-objective integrated production planning and transportation scheduling problem for distributed flexible job shops. A mixed-integer linear programming model is developed to describe the considered problem with the aim of optimizing customer satisfaction and total energy consumption. To address this problem, an enhanced memetic algorithm with a reinforcement learning-driven breeding mechanism (RDMA) is proposed. Unlike existing literature that uses reinforcement learning to adjust parameters or select operators, RDMA marks the initial use of reinforcement learning to recommend the most suitable parents for breeding offspring. Additionally, a knowledge-driven adaptive variable neighborhood search is designed to make incremental improvements to the best solutions and continuously enhance RDMA’s local search performance. Comparative results highlight the benefit of the reinforcement learning-based breeding mechanism and demonstrate the superiority of RDMA over major existing state-of-the-art algorithms. Moreover, experimental analysis indicates that each component in RDMA positively affects search performance, and their collaboration yields the best results.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101945"},"PeriodicalIF":8.2,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903833","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}

{"title":"Listwise ranking predictor for evolutionary neural architecture search","authors":"Nan Li ,&nbsp;Lianbo Ma ,&nbsp;Rui Wang ,&nbsp;Shi Cheng ,&nbsp;Yanan Sun ,&nbsp;Bing Xue ,&nbsp;Mengjie Zhang","doi":"10.1016/j.swevo.2025.101956","DOIUrl":"10.1016/j.swevo.2025.101956","url":null,"abstract":"<div><div>In evolutionary neural architecture search (ENAS), the accuracy predictors (i.e., regression models) have been successfully applied to save computational costs for the evaluation of network architectures. However, the accuracy of these predictors is largely limited by the small amount of evaluated architectures that may be difficult to obtain. Such accuracy predictors with prediction bias often lead to an inaccurate ranking, misleading the selection of ENAS. To alleviate the above limitations, we design an efficient and novel listwise ranking predictor (LRP) for ENAS to directly predict the ranking of each architecture instead of the numerical accuracy value of each architecture. Specifically, the training data is constructed by the proposed random encoding-combination (REC) strategy, which can generate substantial training data using the small number of evaluated architectures (data level). These specially constructed training data are used to train LRP, which can convert the complex regression task into a ranking task to reduce ranking bias (model level). The proposed NAS method is compared with state-of-the-art NAS methods on widely-used benchmark datasets and practical application. Experimental results demonstrate that LRP can alleviate the ranking disorder problem and outperform others in terms of both effectiveness and efficiency.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101956"},"PeriodicalIF":8.2,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902523","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}

{"title":"A coevolutionary algorithm for constrained multi-objective optimization with dynamic relaxation","authors":"Yongchao Li ,&nbsp;Heming Jia ,&nbsp;Hongguang Li","doi":"10.1016/j.swevo.2025.101954","DOIUrl":"10.1016/j.swevo.2025.101954","url":null,"abstract":"<div><div>To effectively address constrained multi-objective problems, algorithms need to strike a balance between objectives and constraints. This article introduces a method that utilizes two separate populations to investigate the exploration of the constrained Pareto front (CPF) and the unconstrained Pareto front (UPF). The fitness of each population is evaluated based on the information entropy of their positions, and suitable evolutionary operators are employed to improve solution quality in terms of convergence and diversity. Moreover, by adaptively relaxing constraint conditions, the auxiliary population can traverse large infeasible domains, thereby enhancing solution diversity. In the initial stages, the auxiliary population evolves alongside the main population, bringing it close to the CPF and minimizing computational resource wastage. A tournament environment selection model based on a dynamic relaxation (DR) function is utilized in the later stages, helping the auxiliary population relax constraints, retain promising solutions, and augment diversity. In addition, an entropy selection evolutionary strategy was designed to address the problem of populations easily falling into local optima during the evolution process. By calculating the entropy information of the population, the current state of the population can be determined, and then appropriate operators can be selected to enable the population to effectively escape from local optimal solutions. Compared against seven state-of-the-art algorithms, demonstrate that the proposed constrained multi-objective optimization evolutionary algorithm (CMOEA) surpasses the performance of existing CMOEAs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101954"},"PeriodicalIF":8.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891942","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}

{"title":"Methodology for optimizing electrical grounding grids in stratified soils using advanced calculation techniques and evolutionary algorithms","authors":"Carlos Leandro Borges da Silva ,&nbsp;Thyago Gumeratto Pires ,&nbsp;Antonio Marcelino Silva Filho ,&nbsp;Junio Santos Bulhões ,&nbsp;Orlando M. Oliveira Belo ,&nbsp;Clóves Gonçalves Rodrigues ,&nbsp;Antonio Paulo Coimbra ,&nbsp;Wesley Pacheco Calixto","doi":"10.1016/j.swevo.2025.101953","DOIUrl":"10.1016/j.swevo.2025.101953","url":null,"abstract":"<div><div>This paper presents a practical methodology for optimizing the geometry of electrical grounding grids at industrial frequencies of <span><math><mrow><mn>50</mn><mspace></mspace><mi>Hz</mi></mrow></math></span> and <span><math><mrow><mn>60</mn><mspace></mspace><mi>Hz</mi></mrow></math></span>, integrating advanced calculation techniques and evolutionary algorithms to improve the safety and operational performance of electrical grounding systems. The proposed approach is particularly beneficial for industrial automation and control systems, where effective grounding is necessary to maintain system reliability and prevent downtime. This methodology employs mathematical modeling and computational tools to optimize grid parameters, ensuring compliance with safety standards while reducing operational costs, thus contributing to the overall efficiency of automated systems in industrial environments. The study reports a reduction of up to 66% in the number of vertical rods and 40% in horizontal conductors compared to traditional methods. These results indicate that the proposed methodology can significantly reduce material usage and costs while maintaining electrical safety in accordance with regulatory standards, making it applicable to a wide range of industrial settings, including substations and automated facilities.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101953"},"PeriodicalIF":8.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887836","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}

{"title":"Enhancement of collaborative communication using optimized dynamic density graph convolutional networks in IoT-enabled intelligent surfaces","authors":"Harish Kumar Taluja ,&nbsp;Anuradha Taluja ,&nbsp;Dhakshnamoorthy Muthukumaran ,&nbsp;Padmanaban K","doi":"10.1016/j.swevo.2025.101909","DOIUrl":"10.1016/j.swevo.2025.101909","url":null,"abstract":"<div><div>Reconfigurable intelligent surface (RIS) is defined as a software-controlled passive devices acts as a relay system, reflecting receiving signals from the source to destination in a cooperative mode with optimal signal strength. An IoT-dependent network's configurable and flexible RIS allows for stand alone or cooperative arrangements with significant advantages over traditional networks. Despite its potential, efficient implementation of RISs in Internet-of-Things (IoT)-based networks remains a difficulty because to the complexity associated with phase shift optimisation and symbol identification. In this manuscript, Enhancement of Collaborative Communication using Optimized Dynamic Density Graph Convolutional Networks in IoT-Enabled Intelligent Surfaces (ECC-DDGNN-IoT IS) is proposed. It focuses on optimising the RIS phase shifts through Dynamic Density Graph Convolutional Networks (DDGNN) approaches. This optimisation increases the signal quality and overall system performance in cooperative communication circumstances. This model addresses the complexity of Maximum Livelihood (ML) detection at destination. A DDGNN-based symbol identification method is introduced, along with DDGNN-assisted phase optimisation of the RIS. This technique decreases computational load on the receiver while retaining good detection accuracy. Therefore, Artificial Protozoa Optimizer (APO) is proposed to optimize the weight parameter of DDGNN model to accurately shift the phase of RIS. This model is implemented in MATLAB platform. The proposed ECC-DDGNN-IoT-IS method attains high accuracy, low RMSE and computational complexity compared to the existing techniques, such as deep-learning enabled IoT depend RIS for cooperative communications (DNN-IoT-RISCC), Deep Learning dependent Detection on Reconfigurable intelligent surface Assisted RSM and RSSK (BDNN-RIS-RSSK), and semi-federated learning in massive IoT networks for collaborative intelligence (SFL-CI-MIoT) respectively.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101909"},"PeriodicalIF":8.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887835","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}