Swarm and Evolutionary Computation最新文献_第9页

Particle Swarm Optimization for a redundant repairable machining system with working vacations and impatience in a multi-phase random environment 在多阶段随机环境中，针对具有工作假期和不耐烦情绪的冗余可修复加工系统的粒子群优化技术

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-05 DOI: 10.1016/j.swevo.2024.101688

Amina Angelika Bouchentouf , Kamlesh Kumar , Parmeet Kaur Chahal

{"title":"Particle Swarm Optimization for a redundant repairable machining system with working vacations and impatience in a multi-phase random environment","authors":"Amina Angelika Bouchentouf , Kamlesh Kumar , Parmeet Kaur Chahal","doi":"10.1016/j.swevo.2024.101688","DOIUrl":"10.1016/j.swevo.2024.101688","url":null,"abstract":"<div><p>With the increasing reliance on cloud computing as the foundational manufacturing systems with intricate dynamics, featuring multiple service areas, varying job arrival rates, diverse service requirements, and the interplay of failures and impatience, significant analytical challenges arise. Queueing networks offer a powerful stochastic modeling framework to capture such complex dynamics. This paper develops a novel, exhaustive queueing model for a finite-capacity redundant multi-server system operating in a multi-phase random environment. The proposed model uniquely integrates real-world factors, including server breakdowns and repairs, waiting servers, synchronous working vacations, and state dependent balking and reneging, into a single queueing model, representing a significant advancement in the field. Using the matrix-analytic method, we establish the steady-state solution and derive key performance metrics. Numerical experiments and sensitivity analyses elucidate the impact of system parameters on performance measures. Additionally, a cost model is formulated, enabling cost optimization analysis using direct search method and Particle Swarm Optimization (PSO) to identify efficient operating configurations.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101688"},"PeriodicalIF":8.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935100","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

Multimodal multiobjective differential evolution algorithm based on enhanced decision space search 基于增强型决策空间搜索的多模式多目标差分进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-05 DOI: 10.1016/j.swevo.2024.101682

Jing Liang , Xudong Sui , Caitong Yue , Mingyuan Yu , Guang Li , Mengmeng Li

{"title":"Multimodal multiobjective differential evolution algorithm based on enhanced decision space search","authors":"Jing Liang , Xudong Sui , Caitong Yue , Mingyuan Yu , Guang Li , Mengmeng Li","doi":"10.1016/j.swevo.2024.101682","DOIUrl":"10.1016/j.swevo.2024.101682","url":null,"abstract":"<div><p>Multimodal multiobjective optimization problems (MMOPs) have attracted extensive research interest. These problems are characterized by the presence of multiple equivalent optimal solutions in the decision space, all corresponding to the same optimal values in the objective space. However, effectively finding a high-quality and evenly distributed Pareto sets (PSs) remains a challenge for researchers. This paper introduces a multimodal multiobjective differential evolution algorithm based on enhanced decision space search (MMODE_EDSS). By adopting two types of strategies to enhance the decision space search capability, the algorithm generates multiple high-quality non-dominated solutions. In the early stages of evolution, neighborhood information is used to enhance search capabilities, while in the later stages, data interpolation methods following clustering are employed for searching. Moreover, to improve the overall population distribution, an environmental selection mechanism based on dual-space crowding distance is adopted. The effectiveness of the proposed algorithm, MMODE_EDSS, is evaluated by comparing it with eight state-of-the-art multimodal multiobjective evolutionary algorithms (MMOEAs). Experimental results confirm the significant advantages of MMODE_EDSS.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101682"},"PeriodicalIF":8.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935101","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

PSO-based lightweight neural architecture search for object detection 基于 PSO 的轻量级目标检测神经架构搜索

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-02 DOI: 10.1016/j.swevo.2024.101684

Tao Gong, Yongjie Ma

{"title":"PSO-based lightweight neural architecture search for object detection","authors":"Tao Gong, Yongjie Ma","doi":"10.1016/j.swevo.2024.101684","DOIUrl":"10.1016/j.swevo.2024.101684","url":null,"abstract":"<div><p>In recent years, Neural Architecture Search (NAS) has received widespread attention for its remarkable ability to design neural networks. However, existing NAS works have mainly focused on network search, with limited emphasis on downstream applications after discovering efficient neural networks. In this paper, we propose a lightweight search strategy based on the particle swarm optimization algorithm and apply the searched network as backbone for object detection tasks. Specifically, we design a lightweight search space based on Ghostconv modules and improved Mobileblocks, achieving comprehensive exploration within the search space using variable-length encoding strategy. During the search process, to balance network performance and resource consumption, we propose a multi-objective fitness function and incorporated the classification accuracy, parameter size, and FLOPs of candidate individuals into optimization. For particle performance evaluation, we propose a new strategy based on weight sharing and dynamic early stopping, significantly accelerating the search process. Finally, we fine-tune the globally optimal particle decoded as the backbone, adding Ghost PAN feature fusion modules and detection heads to build an object detection model, and we achieve a 17.01% mAP on the VisDrone2019 dataset. Experimental results demonstrate the competitiveness of our algorithm in terms of search time and the balance between accuracy and efficiency, and also confirm the effectiveness of object detection models designed through NAS methods.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101684"},"PeriodicalIF":8.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935026","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

Photovoltaic model parameters identification using diversity improvement-oriented differential evolution 利用面向多样性改进的微分演化识别光伏模型参数

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-02 DOI: 10.1016/j.swevo.2024.101689

Chongle Ren, Zhenghao Song, Zhenyu Meng

{"title":"Photovoltaic model parameters identification using diversity improvement-oriented differential evolution","authors":"Chongle Ren, Zhenghao Song, Zhenyu Meng","doi":"10.1016/j.swevo.2024.101689","DOIUrl":"10.1016/j.swevo.2024.101689","url":null,"abstract":"<div><p>Fast and accurate parameter identification of the photovoltaic (PV) model is crucial for calculating, controlling, and managing PV generation systems. Numerous meta-heuristic algorithms have been applied to identify unknown parameters due to the multimodal and nonlinear characteristics of the parameter identification problems. Although many of them can obtain satisfactory results, problems such as premature convergence and population stagnation still exist, influencing the optimization performance. A novel variant of Differential Evolution, namely, Diversity Improvement-Oriented Differential Evolution (DIODE), is proposed to mitigate these deficiencies and obtain reliable parameters for PV models. In DIODE, an adaptive perturbation strategy is employed to perturb current individuals to mitigate premature convergence by enhancing population diversity. Secondly, a diversity improvement mechanism is proposed, where information on the covariance matrix and fitness improvement of individuals is used as a diversity indicator to detect stagnant individuals, which are then updated by the intervention strategy. Lastly, a novel parameter adaptation strategy is employed to maintain a sound balance between exploration and exploitation. The proposed DIODE algorithm is applied to parameter identification problems of six PV models, including single, double, and triple diode and three PV module models. In addition, a large test bed containing 72 benchmark functions from CEC2014, CEC2017, and CEC2022 test suites is employed to verify DIODE’s overall performance in terms of optimization accuracy. Experiment results demonstrate that DIODE can secure accurate parameters of PV models and achieve highly competitive performance on benchmark functions.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101689"},"PeriodicalIF":8.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935103","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 hybrid genetic tabu search algorithm for distributed job-shop scheduling problems 针对分布式作业车间调度问题的混合遗传塔布搜索算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-31 DOI: 10.1016/j.swevo.2024.101670

Jin Xie, Liang Gao, Xinyu Li, Lin Gui

{"title":"A hybrid genetic tabu search algorithm for distributed job-shop scheduling problems","authors":"Jin Xie, Liang Gao, Xinyu Li, Lin Gui","doi":"10.1016/j.swevo.2024.101670","DOIUrl":"10.1016/j.swevo.2024.101670","url":null,"abstract":"<div><p>The distributed job-shop scheduling problem (DJSP) is an extension of the traditional job-shop scheduling problem, which are composed of two sub-problems, assigning jobs to suitable factories and deciding the operation sequence on machines. To evaluate the performance of algorithms for solving DJSP, several famous benchmark instances have been proposed, and most of these instances have not been solved so far. This paper proposes a hybrid genetic tabu search algorithm (HGTSA) for solving DJSP. The proposed HGTSA combines the global search ability of the genetic algorithm (GA) and the local search ability of the tabu search (TS) well. In GA part, a crossover operation and a mutation operation are devised based on the critical factory. The two operations can effectively improve the discreteness of the population. In TS part, a tabu search procedure is performed on the critical factory. The procedure can effectively enhance the local search ability of HGTSA. For evaluating the performance of HGTSA, it has been compared with five classical algorithms on 240 benchmark instances. The computational results show the efficiency and effectiveness of HGTSA for solving DJSP. In particular, the proposed HGTSA updates the upper bounds for 235 out of these difficult instances.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101670"},"PeriodicalIF":8.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935105","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 evolutionary multi-tasking band selection algorithm for hyperspectral image classification 用于高光谱图像分类的多目标进化多任务波段选择算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-29 DOI: 10.1016/j.swevo.2024.101665

Qijun Wang , Yong Liu , Ke Xu , Yanni Dong , Fan Cheng , Ye Tian , Bo Du , Xingyi Zhang

{"title":"Multi-objective evolutionary multi-tasking band selection algorithm for hyperspectral image classification","authors":"Qijun Wang , Yong Liu , Ke Xu , Yanni Dong , Fan Cheng , Ye Tian , Bo Du , Xingyi Zhang","doi":"10.1016/j.swevo.2024.101665","DOIUrl":"10.1016/j.swevo.2024.101665","url":null,"abstract":"<div><p>Hyperspectral images (HSI) contain a great number of bands, which enable better characterization of features. However, the huge dimension and information volume brought by the abundant bands may give rise to a negative influence on the efficiency of subsequent processing on hyperspectral images. Band selection (BS) is a commonly adopted to reduce the dimension of HSIs. Different from the previous work, in this paper, hyperspectral band selection problem is formulated as a multi-objective optimization problem, where the band distribution uniformity among the selected bands and inter-class separation distance from a few labeled samples are optimized simultaneously. To fully exploit the relation between the band subsets with different sizes, we construct a multi-objective evolutionary multi-tasking algorithm for hyperspectral band selection (namely MEMT-HBS) to achieve the selected band subsets for all the selected band sizes in one run. To implement MEMT-HBS, the intra-task pairwise learning based solution generation strategy is suggested to evolve the population for each task to achieve high-quality offspring whose selected band size is restricted to a fixed scope. The inter-task band coverage based knowledge transferring strategy is utilized to choose useful individuals from adjacent tasks to further enhance the performance of current task. Compared with the state-of-the-art semi-supervised and unsupervised BS algorithms, empirical results on different standard hyperspectral datasets show that our proposed MEMT-HBS can determine the superior band subset which has a higher image classification accuracy over the comparison algorithms.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101665"},"PeriodicalIF":8.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935104","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 cooperative learning-aware dynamic hierarchical hyper-heuristic for distributed heterogeneous mixed no-wait flow-shop scheduling 分布式异构混合无等待流-shop调度的合作学习感知动态分层超启发式

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-29 DOI: 10.1016/j.swevo.2024.101668

Ningning Zhu , Fuqing Zhao , Yang Yu , Ling Wang

{"title":"A cooperative learning-aware dynamic hierarchical hyper-heuristic for distributed heterogeneous mixed no-wait flow-shop scheduling","authors":"Ningning Zhu , Fuqing Zhao , Yang Yu , Ling Wang","doi":"10.1016/j.swevo.2024.101668","DOIUrl":"10.1016/j.swevo.2024.101668","url":null,"abstract":"<div><p>The distributed heterogeneous mixed scheduling mode in the manufacturing systems emphasizes the cooperation between factories for the entire production cycle, which poses enormous challenges to the processing and assignment of jobs. Discrepancies in the processing environment and types of machines of each factory during various production stages cause diverse processing paths and scheduling. The distributed heterogeneous mixed no-wait flow-shop scheduling problem with sequence-dependent setup time (DHMNWFSP-SDST), abstracted from the industrial scenarios, is addressed in this paper. The mathematical model of DHMNWFSP-SDST is established. A cooperative learning-aware dynamic hierarchical hyper-heuristic (CLDHH) is proposed to solve the DHMNWFSP-SDST. In CLDHH, a cooperative initialization method is developed to promote diversity and quality of solutions. A hierarchical hyper-heuristic framework with reinforcement learning (RL) is designed to select the algorithm component automatically. Estimation of Distribution Algorithm (EDA) guides the upper-layer RL to select four neighborhood structures. A dynamic adaptive neighborhood switching constructs the lower-layer RL to accelerate exploitation with the dominant sub-neighborhoods. An elite-guided hybrid path relinking achieves local enhancement. The experimental results of CLDHH and six state-of-the-art algorithms on instances indicate that the proposed CLDHH is superior to the state-of-the-art algorithms in solution quality, robustness, and efficiency.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101668"},"PeriodicalIF":8.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935106","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

Two-stage knowledge-assisted coevolutionary NSGA-II for bi-objective path planning of multiple unmanned aerial vehicles 用于多无人飞行器双目标路径规划的两阶段知识辅助协同进化 NSGA-II

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-27 DOI: 10.1016/j.swevo.2024.101680

Tianwei Zhou , Zhenghan Zhou , Haiyun Qiu , Ben Niu , Gabriel Xiao-Guang Yue , Witold Pedrycz

{"title":"Two-stage knowledge-assisted coevolutionary NSGA-II for bi-objective path planning of multiple unmanned aerial vehicles","authors":"Tianwei Zhou , Zhenghan Zhou , Haiyun Qiu , Ben Niu , Gabriel Xiao-Guang Yue , Witold Pedrycz","doi":"10.1016/j.swevo.2024.101680","DOIUrl":"10.1016/j.swevo.2024.101680","url":null,"abstract":"<div><p>This paper focuses on the bi-objective path planning problem of multiple unmanned aerial vehicles (UAVs) under the complex environment with numerous obstacles and threat areas, where the UAVs need to be kept as far away as possible from threat areas during flight. Based on the integrated energy reduction perspective, a bi-objective model is subtly constructed by minimizing the total energy consumption of each path (including flight altitude, horizontal turns, and path length), and minimizing the costs of the total threats (including ground radar, anti-aircraft gun, missile and geological hazard threat areas). Moreover, a two-stage knowledge-assisted coevolutionary NSGA-II algorithm is novelly proposed to enhance collaboration and avoid collision. The first stage is designed for population convergence, where the considered constrained problem is solved with the help of the designed problem without the constraints of threats and obstacles. The second stage emphasizes the quality and diversity of solutions. In this stage, a double-population coevolution approach is developed. Additionally, a multi-mode strategy is introduced for the inferior population, leveraging reinforcement learning. This strategy aids in selecting the optimal mode from random swing, directed guidance, and potential dominance exploration. Furthermore, experimental results in two different environments show that the proposed algorithm can better solve the collaborative path planning problem for multiple UAVs compared with other five classical or recent proposed algorithms.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101680"},"PeriodicalIF":8.2,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141776940","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 many-objective evolutionary algorithm based on interaction force and hybrid optimization mechanism 基于相互作用力和混合优化机制的多目标进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-25 DOI: 10.1016/j.swevo.2024.101667

Lei Yang , Jiale Cao , Kangshun Li , Yuanye Zhang , Rui Xu , Ke Li

{"title":"A many-objective evolutionary algorithm based on interaction force and hybrid optimization mechanism","authors":"Lei Yang , Jiale Cao , Kangshun Li , Yuanye Zhang , Rui Xu , Ke Li","doi":"10.1016/j.swevo.2024.101667","DOIUrl":"10.1016/j.swevo.2024.101667","url":null,"abstract":"<div><p>In many-objective optimization, both convergence and diversity are equally important. However, in high-dimensional spaces, traditional decomposition-based many-objective evolutionary algorithms struggle to ensure population diversity. Conversely, traditional Pareto dominance-based many-objective evolutionary algorithms face challenges in ensuring population convergence. In this paper, we propose a novel many-objective evolutionary algorithm based on interaction force and hybrid optimization mechanism (MaOEAIH) for effectively addressing the difficulty in balancing convergence and diversity. First, we use the concept of interaction force to simulate the convergence (akin to gravity) and diversity (repulsion) of the population. Subsequently, we design an optimization mechanism that combines decomposition and Pareto dominance to enhance the convergence and diversity of the population separately. Simultaneously, to eliminate dominance resistance solutions, we propose a quartile method based on boundary solutions. Additionally, Random perturbations are also introduced to certain individuals within the population to facilitate their escape from local optima. MaOEAIH is compared with some state-of-the-art algorithms on 31 well-known test problems with 3-15 objectives. The experimental results show that, compared to other algorithms, MaOEAIH not only obtains solution sets of higher quality when dealing with different types of many-objective optimization problems, but also effectively addresses key challenges including insufficient selection pressure, difficulty balancing convergence and diversity, and susceptibility to population entrapment in local optima within many-objective optimization scenarios.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101667"},"PeriodicalIF":8.2,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141776941","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

Two-sided resource-constrained assembly line balancing problem: a new mathematical model and an improved genetic algorithm 双面资源受限的装配线平衡问题：一种新的数学模型和改进的遗传算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-25 DOI: 10.1016/j.swevo.2024.101662

Linhao Feng, Yesong Wang, Xifeng Fang, Hang Yu, Shengwen Zhang

{"title":"Two-sided resource-constrained assembly line balancing problem: a new mathematical model and an improved genetic algorithm","authors":"Linhao Feng, Yesong Wang, Xifeng Fang, Hang Yu, Shengwen Zhang","doi":"10.1016/j.swevo.2024.101662","DOIUrl":"10.1016/j.swevo.2024.101662","url":null,"abstract":"<div><p>Two-sided assembly lines are typically employed in the production of medium and large-sized products with the aim of reducing the length of the assembly line, enhancing assembly efficiency and consequently reducing the time required for product assembly. However, traditional Two-sided assembly lines lack effective resource scheduling management methods in production scheduling, which results in low productivity and high resource costs. In order to address this issue, we propose a new two-sided resource-constrained assembly line balancing problem (TRCLBP) model. The model takes the minimum number of workstations and the minimum assembly cost as its objective function and proposes an improved genetic algorithm (I-GA) to solve it. A three-layer chromosome initialization method is proposed for the assembly tasks and resource decisions, which effectively improves the diversity and quality of the initial population. Furthermore, the algorithm employs strategies such as matching crossover and redistributing variants to ensure rapid convergence of the populations and to prevent them from falling into local optimums. Finally, the efficacy of the model and algorithm proposed in this paper is validated through a comprehensive analysis of arithmetic case studies and enterprise engineering examples. This analysis reveals a reduction of approximately 18 % in the total cost of assembly. Furthermore, the model enables enterprises to make informed decisions regarding the optimal allocation of resources, thereby reducing production costs and improving the efficiency of assembly operations during periods of expansion.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101662"},"PeriodicalIF":8.2,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141776942","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