Swarm and Evolutionary Computation最新文献_第6页

Collaborative knowledge transfer-based multiobjective multitask particle swarm optimization 基于协同知识转移的多目标多任务粒子群优化

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-05 DOI: 10.1016/j.swevo.2025.102115

Yushuang Wang , Zheng Liu , Honggui Han

{"title":"Collaborative knowledge transfer-based multiobjective multitask particle swarm optimization","authors":"Yushuang Wang , Zheng Liu , Honggui Han","doi":"10.1016/j.swevo.2025.102115","DOIUrl":"10.1016/j.swevo.2025.102115","url":null,"abstract":"<div><div>Evolutionary multitask optimization (EMTO) has been an emerging optimization paradigm to handle several different optimization problems in parallel by utilizing knowledge transfer. However, most existing EMTO algorithms focus only on facilitating knowledge transfer in the search space to deal with multiple optimization tasks, while ignoring the potential relationship problem in the objective space, which may lead to the degradation of knowledge transfer performance, especially for multiobjective EMTO. To address this problem, a collaborative knowledge transfer-based multiobjective multitask particle swarm optimization (CKT-MMPSO) is designed in this paper. First, a CKT-MMPSO scheme is introduced to comprehensively exploit the knowledge from different spaces to solve multiple optimization problems. Then, the knowledge transfer can be effectively implemented to improve the quality of solutions. Second, a bi-space knowledge reasoning method is developed to make full use of population distribution information in the search space and particle evolutionary information in the objective space. Then, the search space knowledge and the objective space knowledge can be acquired to assist in the knowledge transfer. Third, an information entropy-based collaborative knowledge transfer mechanism is designed to balance convergence and diversity. Then, knowledge transfer patterns can be adaptively performed in different evolutionary stages to generate promising solutions. Finally, CKT-MMPSO is applied to some benchmark problems to verify its effectiveness. Furthermore, compared with other state-of-the-art algorithms, several experiments demonstrate that CKT-MMPSO can achieve the desirable performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102115"},"PeriodicalIF":8.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781507","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

Heuristic-guided scatter search for X-architecture Steiner Minimum Tree problems in VLSI design 超大规模集成电路设计中x结构Steiner最小树问题的启发式引导散射搜索

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-08-04 DOI: 10.1016/j.swevo.2025.102088

Kai Yang, Fuyuan Zheng, Qingjin Ji, Juan Lin, Yiwen Zhong, Yu Lin

{"title":"Heuristic-guided scatter search for X-architecture Steiner Minimum Tree problems in VLSI design","authors":"Kai Yang, Fuyuan Zheng, Qingjin Ji, Juan Lin, Yiwen Zhong, Yu Lin","doi":"10.1016/j.swevo.2025.102088","DOIUrl":"10.1016/j.swevo.2025.102088","url":null,"abstract":"<div><div>Constructing Steiner Minimum Trees (SMT) remains a critical challenge in Very Large Scale Integration (VLSI) global routing, where minimizing wirelength is essential for optimizing circuit performance. While traditional Manhattan-based SMTs are constrained to two orthogonal routing directions, resulting in suboptimal interconnects, the X architecture, with its eight-directional (four rectilinear, four diagonal) routing, enables significant wirelength reductions. This paper introduces a Heuristic-Guided Scatter Search (HGSS) algorithm to efficiently solve the X-architecture SMT (XSMT) problem. The HGSS integrates a short-edge-first heuristic to prioritize compact routing solutions and reengineers three core Scatter Search modules: (1) a Dynamic Reference Set Update Module (DRSUM) that maintains elite and diverse solutions through iterative replacement, (2) a Semi-systematic Subset Generation Module (SSGM) pairing diverse and random elite solutions to reduce computational overhead, and (3) a Heuristic-Guided Solution Combination Module (HGSCM) employing crossover and mutation to generate high-quality offspring. Evaluations of GEO and ISPD98 benchmark circuits demonstrate average wirelength reductions of 1.04% and 2.86%, respectively, along with superior computational efficiency compared to state-of-the-art methods. By advancing XSMT optimization, this work demonstrates that incorporating heuristic information is valuable for solving large, complex routing tree problems, offering guidance for further research in this area.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102088"},"PeriodicalIF":8.5,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771534","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 manifold prediction strategy for evolutionary dynamic multiobjective optimization 进化动态多目标优化的流形预测策略

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-07-31 DOI: 10.1016/j.swevo.2025.102103

Huaqiang Xu, Zhen Xu, Yumeng Wang, Lijun Li, Yuefeng Zhao, Jingjing Wang

{"title":"A manifold prediction strategy for evolutionary dynamic multiobjective optimization","authors":"Huaqiang Xu, Zhen Xu, Yumeng Wang, Lijun Li, Yuefeng Zhao, Jingjing Wang","doi":"10.1016/j.swevo.2025.102103","DOIUrl":"10.1016/j.swevo.2025.102103","url":null,"abstract":"<div><div>Prediction-based evolutionary algorithms have shown impressive effectiveness in solving dynamic multiobjective optimization problems (DMOPs). Typically, these algorithms utilize the historical information of specific representative points, such as center and knee points, to predict the moving trend of the Pareto-optimal set (PS). However, the changing pattern of PS may be inconsistent with that of the representative points, potentially leading to inaccurate prediction of the new PS. Manifold learning captures the overall distribution of PS. Therefore, the changing trend of the manifold reflects the changing pattern of PS. This work introduces a manifold prediction strategy (MPS) for evolutionary dynamic multiobjective optimization algorithms. The MPS predicts the manifold of the PS in a new environment based on the trend observed in historical PSs. Specifically, the Local Principal Component Analysis (LPCA) algorithm is enhanced to learn the manifolds of historical PSs. Using these learned manifolds, MPS estimates the manifold of the PS in the new environment with a linear prediction model. Recognizing that the accuracy of manifold learning results will affect the accuracy of manifold prediction, two methods are proposed to improve the learning results. These methods focus on determining an appropriate number of local manifolds and reducing the randomness during the modeling process. The proposed MPS is tested and compared with several state-of-the-art dynamic multiobjective evolutionary algorithms on various benchmark test instances. Experimental results indicate that MPS outperforms other algorithms on most instances.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102103"},"PeriodicalIF":8.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738221","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

Simultaneous optimization for berth and quay crane scheduling in container terminals 集装箱码头泊位与岸机调度同步优化

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-07-31 DOI: 10.1016/j.swevo.2025.102091

Saeideh Amirifar , Ali Tavakoli Kashani , Ali Omidvarpanah Ahmadabadi , Erfan Hassannayebi

{"title":"Simultaneous optimization for berth and quay crane scheduling in container terminals","authors":"Saeideh Amirifar , Ali Tavakoli Kashani , Ali Omidvarpanah Ahmadabadi , Erfan Hassannayebi","doi":"10.1016/j.swevo.2025.102091","DOIUrl":"10.1016/j.swevo.2025.102091","url":null,"abstract":"<div><div>The rising growth of the global economy along with maritime trade prosperity highlight the resilience of this industry despite challenges. Seaports, as prominent hubs of the international supply chain, handle approximately 80 % of global freight volume which necessitates the efficient planning and scheduling of operations. This study addresses the functional and deep integrated planning of the berth allocation problem (BAP), quay crane assignment problem (QCAP), and quay crane scheduling problem (QCSP). Also, a novel algorithm for scheduling the QCs within the deep integrated scheme was developed. A comprehensive comparison of integration approaches for the three problems’ MILP model was proposed utilizing hybrid solution algorithms with Particle Swarm Optimization (PSO) and Dynamic Programming (DP) methods. The objective functions aimed at minimizing the costs of berth position deviation from the best berth location, deviation of end time from departure time, costs of services during berth time, and the setup number of QCs. Findings demonstrated that the deep integrated model with developed QCSP algorithm and classic PSO solution demonstrates superior cost optimization compared to hybrid methods which prioritize computational efficiency at the expense of solution quality, particularly in large-scale scenarios. The trade-off between solution quality and runtime of the classic PSO, underscores the algorithm's strengths for offline optimization and significant cost savings in strategic planning but highlights challenges in dynamic, real-time applications. The effectiveness and practicality of the proposed algorithms were also validated and future research suggestions provided for more developments.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102091"},"PeriodicalIF":8.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738220","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

Data-driven control law optimization via Kriging surrogate model with adaptive domain reconstruction 基于自适应重构Kriging代理模型的数据驱动控制律优化

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-07-31 DOI: 10.1016/j.swevo.2025.102106

Xinhan Hu , Wenya Zhou , Xiaoming Wang , Zongyu Zhang , Xing Chen , Tianao Zhang

{"title":"Data-driven control law optimization via Kriging surrogate model with adaptive domain reconstruction","authors":"Xinhan Hu , Wenya Zhou , Xiaoming Wang , Zongyu Zhang , Xing Chen , Tianao Zhang","doi":"10.1016/j.swevo.2025.102106","DOIUrl":"10.1016/j.swevo.2025.102106","url":null,"abstract":"<div><div>Data-driven control parameter design methods rely on an appropriate initial design domain, which is challenging to define for complex systems with poorly understood dynamics. This reliance creates a dilemma: overly large domains risk instability and high computational costs, while conservative domains may exclude global optimal solutions. To address this issue, a new data-driven control law design method is proposed, combining Kriging surrogate optimization with a dual-mode design domain adaptive reconstruction (DAR) strategy. Taking Active Disturbance Rejection Control (ADRC) as an example, a data-driven Kriging surrogate-based design framework is constructed with control parameters as inputs and control performance index as output. The proposed method dynamically relocates and resizes the search space through stability-constrained boundary adjustments, eliminating dependence on empirical domain settings. Experimental validation on several numerical benchmark problems and two control system applications reveals that the proposed method offers enhanced optimization efficiency and superior global convergence. Its robust adaptability to diverse extreme initial domains effectively lowers the barriers to engineering applications of control law design. This work provides a new reference for future control system design with high-dimensional nonlinear dynamics by bridging the gap between data-driven exploration and deterministic control approaches.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102106"},"PeriodicalIF":8.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738219","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 adaptive hybrid MEMD-Transformer-BiGRU model using MPA algorithm for air quality prediction 基于MPA算法的MEMD-Transformer-BiGRU混合自适应空气质量预测模型

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-07-29 DOI: 10.1016/j.swevo.2025.102099

Yong Zhang , Wentao Cheng , Jie Sun , Li Wang , Shurui Fan , Jingyu Zhang , Shuhao Jiang

{"title":"An adaptive hybrid MEMD-Transformer-BiGRU model using MPA algorithm for air quality prediction","authors":"Yong Zhang , Wentao Cheng , Jie Sun , Li Wang , Shurui Fan , Jingyu Zhang , Shuhao Jiang","doi":"10.1016/j.swevo.2025.102099","DOIUrl":"10.1016/j.swevo.2025.102099","url":null,"abstract":"<div><div>Accurate air quality prediction is usually difficult to achieve because of multiple influencing factors, complex interrelationships and multi-scale processing behaviors. In this study, an adaptive hybrid modelling framework was proposed, which combines Multivariate Empirical Mode Decomposition (MEMD), and Transformer-Bidirectional Gated Recurrent Unit (BiGRU) architectures optimized by the Marine Predators (MPA) Algorithm.</div><div>Firstly, a multi-dimensional feature matrix is constructed considering the multiple input variables such as various pollutants, meteorological factors and air quality indices (AQI). And the MEMD method is used for the matrix decomposition and nonlinear coupling features effective extraction, which could reveal deep coupling relationships and spatiotemporal variation patterns from the multi-source heterogeneous data and the Intrinsic Mode Functions (IMFs) could be obtained.</div><div>Subsequently, a heterogeneous prediction model combining Transformer and BiGRU networks is designed to capture the overall trends and features of the IMFs. In which, the MPA is utilized for the parameters optimization and an adaptive BiGRU network is employed for the weights dynamical adjusting to emphasize the relative importance of each IMF over time.</div><div>Finally, the experiments were given and analyzed with the air quality datasets in Beijing,Tianjin and Shijiazhuang. The results demonstrated that the proposed hybrid model exhibits remarkable efficacy in features extraction, with a root mean square error (RMSE) of 3.1175, mean absolute percentage error (MAPE) of 1.8731 %, and mean absolute error (MAE) of 2.0258, outperforming other comparative models in all comprehensive metrics. Consequently, this hybrid model can effectively improve prediction accuracy and better capture important characteristic information with meteorological factors.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102099"},"PeriodicalIF":8.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722722","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 Generational Difference Vector based Tri-Entropy Structure Optimizer for large-scale multiobjective optimization 基于代际差向量的大规模多目标优化三熵结构优化器

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-07-29 DOI: 10.1016/j.swevo.2025.102079

Yuhan Xu, Yu Zhang, Wang Hu

{"title":"A Generational Difference Vector based Tri-Entropy Structure Optimizer for large-scale multiobjective optimization","authors":"Yuhan Xu, Yu Zhang, Wang Hu","doi":"10.1016/j.swevo.2025.102079","DOIUrl":"10.1016/j.swevo.2025.102079","url":null,"abstract":"<div><div>The increasing complexity of large-scale multiobjective optimization problems in engineering and scientific fields in recent years has imposed higher demands on the computational efficiency of algorithms. This paper introduces a novel algorithm named the Generational Difference Vector based Tri-Entropy Structure Optimizer (GDVTSO), which is designed for the efficient execution of large-scale multi-objective optimization tasks. The core idea is to determine a more effective search direction by calculating the local information entropy within the decision space and analyzing the changes in clusters before and after iterations. To this end, the Tri-Entropy Structure Optimizer (TSO) has been designed to more efficiently utilize information entropy for vector updates. Furthermore, the Generational Difference Vector (GDV) mechanism is introduced to provide guidance on search direction for vectors within each cluster. The GDVTSO algorithm demonstrates exceptional compatibility and extensive application potential. In this study, GDVTSO is integrated with two established large-scale optimization techniques, and a hybrid algorithm designated as GDVTSF is proposed through this methodological fusion. Moreover, GDVTSF’s performance exhibits a lower sensitivity to the dimensionality of optimization problems. Experimental results on standard large-scale multiobjective optimization benchmarks demonstrate that GDVTSF outperforms the current state-of-the-art optimization algorithms. Furthermore, it remarkably maintains its superior performance even when applied to high-dimensional problems with up to 10,000 decision variables.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102079"},"PeriodicalIF":8.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722720","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 Tree neural network deep reinforcement learning for flexible job shop scheduling with transportation constraints 具有运输约束的柔性作业车间调度的树神经网络深度强化学习

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-07-28 DOI: 10.1016/j.swevo.2025.102102

Yadian Geng, Ning Zhao

{"title":"A Tree neural network deep reinforcement learning for flexible job shop scheduling with transportation constraints","authors":"Yadian Geng, Ning Zhao","doi":"10.1016/j.swevo.2025.102102","DOIUrl":"10.1016/j.swevo.2025.102102","url":null,"abstract":"<div><div>The Flexible Job Shop Scheduling Problem with Transportation Constraints (FJSP-T) is critical for improving productivity in flexible manufacturing systems, particularly when automated guided vehicles (AGVs) are involved. This study focuses on minimizing the makespan by formulating the FJSP-T as a mixed-integer linear programming (MILP) model with explicitly defined constraints and objective functions. To solve large-scale instances efficiently, the problem is further modeled as a Markov Decision Process (MDP), where an agent sequentially selects operations, assigns machines, and allocates AGVs based on the current production state. A key contribution of this work is the development of a novel tree-based deep reinforcement learning algorithm, Hierarchical Scheduling and Transportation Tree (HSTT). Built on the dual deep Q-network (DDQN) framework, HSTT leverages the hierarchical structure of scheduling decisions to reduce encoding complexity and improve learning efficiency. Additionally, a local attention mechanism is integrated into the tree search process to constrain the decision space and enhance policy accuracy. Experimental results on benchmark datasets demonstrate that HSTT significantly outperforms traditional dispatching rules, metaheuristic methods, and existing deep reinforcement learning approaches in terms of makespan, runtime, and generalization performance.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102102"},"PeriodicalIF":8.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716134","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

MOHFDQ: A metaheuristic approach to optimizing hospital patient registration with a fuzzy double-orbit queueing model MOHFDQ：基于模糊双轨排队模型的医院患者挂号优化元启发式方法

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-07-28 DOI: 10.1016/j.swevo.2025.102090

Sibasish Dhibar

{"title":"MOHFDQ: A metaheuristic approach to optimizing hospital patient registration with a fuzzy double-orbit queueing model","authors":"Sibasish Dhibar","doi":"10.1016/j.swevo.2025.102090","DOIUrl":"10.1016/j.swevo.2025.102090","url":null,"abstract":"<div><div>Queueing management is applied across sectors such as airlines, banking, healthcare, and telecommunications. However, there are very few queueing model-based approaches to managing patient waiting times, particularly in healthcare systems. Patients often reattempt to access services when they are initially unable to receive care upon arrival. If the registration desk is busy, an arriving patient may choose to join either the regulaor execuwaiting area. In this study, we consider two types of patients: premium/emergency patients who receive additional services based on payment and ordinary/non-emergency patients, who do not pay extra. However, if an ordinary patient becomes dissatisfied with the service, the registration desk may provide additional support. To address this, the MOHFDQ framework a three-phase approach integrating queueing theory, fuzzy theory, and stochastic optimization developed. In the first phase, the MOHFDQ model is analyzed and key performance metrics such as patient queue length, waiting time, and throughput are established. In the second phase, the crisp model is extended to a fuzzy model for a double orbit system, and α-cut and parametric nonlinear programming (PNLP) techniques are used to derive various fuzzified metrics. Finally, in the third phase, to enhance productivity and ensure high quality of service (QoS), a metaheuristic genetic algorithm (GA) and a deterministic golden section search (GSS) method are implemented to determine the optimal service parameters by minimizing the total system cost.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102090"},"PeriodicalIF":8.5,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722719","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

Multi-objective dynamic economic emission dispatch considering the risk of wind power-electric vehicle interaction using teaching-learning-based optimization algorithm 基于教-学优化算法的考虑风电-电动车交互风险的多目标动态经济排放调度

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-07-26 DOI: 10.1016/j.swevo.2025.102096

Baihao Qiao , Ziru Feng , Zhengyu Zhu , Li Yan , Boyang Qu , Xuzhao Chai , Jiajia Huan

{"title":"Multi-objective dynamic economic emission dispatch considering the risk of wind power-electric vehicle interaction using teaching-learning-based optimization algorithm","authors":"Baihao Qiao , Ziru Feng , Zhengyu Zhu , Li Yan , Boyang Qu , Xuzhao Chai , Jiajia Huan","doi":"10.1016/j.swevo.2025.102096","DOIUrl":"10.1016/j.swevo.2025.102096","url":null,"abstract":"<div><div>As the traditional energy systems dominated by fossil fuels transform into renewable energy, more and more renewable energy, such as wind power, are being integrated into the power system. However, due to the strong randomness of wind power, it will extremely affect the security of the power grid. Electric vehicles (EVs) can mitigate the instability of wind power through their energy storage capabilities, but the charging/discharging behavior of large-scale EVs is intensely unstable. Therefore, to assess the uncertainty of the interaction between wind power and EV (IWEv), a dynamic economic emission dispatch (DEED) model based on the conditional value at risk (CVaR) of IWEv (DEED<sub>R-IWEv</sub>) is proposed considering the pollution emission, fuel cost and the system operation risk. Besides, some practical constraints such as the power balance, the residual power of the EVs, travel constraints for EVs owners, charging and discharging power, and climbing rate are included in DEED<sub>R-IWEv</sub>. To obtain a satisfactory dispatching solution, a self-adaptive multi-mode teaching-learning-based optimization (SaMmTLBO) algorithm is proposed. In SaMmTLBO, the teaching factors in the teacher phase is improved to enhance the diversity, and then a parameter self-adaptive mechanism and multi-model learning are developed in learn phase to increase the efficiency. Finally, the feasibility and effectiveness of the proposed model and algorithm are validated on a 10-unit system.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102096"},"PeriodicalIF":8.2,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713249","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