Swarm and Evolutionary Computation最新文献_第8页

Exploring interpretable evolutionary optimization via significance of each constraint and population diversity 通过各约束条件的重要性和种群多样性探索可解释的进化优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-28 DOI: 10.1016/j.swevo.2024.101679

Yalin Wang , Xujie Tan , Chenliang Liu , Pei-Qiu Huang , Qingfu Zhang , Chunhua Yang

{"title":"Exploring interpretable evolutionary optimization via significance of each constraint and population diversity","authors":"Yalin Wang , Xujie Tan , Chenliang Liu , Pei-Qiu Huang , Qingfu Zhang , Chunhua Yang","doi":"10.1016/j.swevo.2024.101679","DOIUrl":"10.1016/j.swevo.2024.101679","url":null,"abstract":"<div><p>Evolutionary algorithms (EAs) have been widely employed to solve complex constrained optimization problems (COPs). However, numerous EAs treat constraints as a collective black box, employing a uniform processing technique for all constraints. Generally, there exists variability in the significance of each constraint within COPs. To address this issue, this paper is the first attempt to investigate the significance of each constraint spontaneously during the evolution process, and then proposes a co-directed evolutionary algorithm (CdEA-SCPD) for exploring interpretable COPs. First, CdEA-SCPD develops an adaptive penalty function designed to assign different weights to constraints based on their violation severity, thereby varying the significance of each constraint to enhance interpretability and facilitate the algorithm to converge more rapidly toward the global optimum. In addition, a dynamic archiving strategy and a shared replacement mechanism are developed to improve the population diversity of CdEA-SCPD. Extensive experiments on benchmark functions from IEEE CEC2006, CEC2010, and CEC2017 and three engineering problems demonstrate the superiority of the proposed CdEA-SCPD compared to existing competitive EAs. Specifically, on the benchmark functions from IEEE CEC2010, the proposed method yields <span><math><mi>ρ</mi></math></span> values lower than 0.05 in the multiple-problem Wilcoxon's signed rank test and ranks first in the Friedman's test. Furthermore, ablation analysis and parameter analysis have demonstrated the beneficial effects of the proposed strategies.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101679"},"PeriodicalIF":8.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089629","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 indicator-based multi-objective variable neighborhood search approach for query-focused summarization 基于指标的多目标变量邻域搜索方法，用于以查询为重点的汇总

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-28 DOI: 10.1016/j.swevo.2024.101721

Jesus M. Sanchez-Gomez , Miguel A. Vega-Rodríguez , Carlos J. Pérez

{"title":"An indicator-based multi-objective variable neighborhood search approach for query-focused summarization","authors":"Jesus M. Sanchez-Gomez , Miguel A. Vega-Rodríguez , Carlos J. Pérez","doi":"10.1016/j.swevo.2024.101721","DOIUrl":"10.1016/j.swevo.2024.101721","url":null,"abstract":"<div><p>Currently, automatic multi-document summarization is an interesting subject in numerous fields of study. As a part of it, query-focused summarization is becoming increasingly important in recent times. These methods can automatically produce a summary based on a query given by the user, including the most relevant information from the query at the same time as the redundancy among sentences is reduced. This can be achieved by developing and applying a multi-objective optimization approach. In this paper, an Indicator-based Multi-Objective Variable Neighborhood Search (IMOVNS) algorithm has been designed, implemented, and tested for the query-focused extractive multi-document summarization problem. Experiments have been carried out with datasets from Text Analysis Conference (TAC). The results were evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. IMOVNS has greatly improved the results presented in the scientific literature, providing improvement percentages in ROUGE metric reaching up to 69.24% in ROUGE-1, up to 57.70% in ROUGE-2, and up to 77.37% in ROUGE-SU4 scores. Hence, the proposed IMOVNS offers a promising solution to the query-focused summarization problem, thus highlighting its efficacy and potential for enhancing automatic summarization techniques.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101721"},"PeriodicalIF":8.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2210650224002591/pdfft?md5=f34b411ab225ef805d85caf741394919&pid=1-s2.0-S2210650224002591-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the representativeness metric of benchmark problems in numerical optimization 论数值优化基准问题的代表性度量

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-23 DOI: 10.1016/j.swevo.2024.101716

Caifeng Chen , Qunfeng Liu , Yunpeng Jing , Mingming Zhang , Shi Cheng , Yun Li

{"title":"On the representativeness metric of benchmark problems in numerical optimization","authors":"Caifeng Chen , Qunfeng Liu , Yunpeng Jing , Mingming Zhang , Shi Cheng , Yun Li","doi":"10.1016/j.swevo.2024.101716","DOIUrl":"10.1016/j.swevo.2024.101716","url":null,"abstract":"<div><p>Numerical comparison on benchmark problems is often necessary in evaluating optimization algorithms with or without theoretical analysis. An implicit assumption is that the adopted set of benchmark problems is representative. However, to our knowledge, there are few results about how to evaluate the representativeness of a test suite, partly due to the difficulty of this issue. In this paper, we first define three different levels of representativeness, and open up a window for addressing step by step the issue of representativeness-measuring. Then we turn to address the Type-III representativeness-measuring problem, and provide a metric for this problem. To illustrate how to use the proposed metric, the representativeness-measuring problem of benchmark problems for single-objective unconstrained continuous optimization is examined.</p><p>The analysis covers as many as 1141 single-objective unconstrained continuous benchmark problems, primarily focusing on existing benchmark problems. Based on the defined representativeness metric, some classical features and calculations are used to assess the representativeness of the benchmark problems. Assessment results show that most of the benchmark problems of high representativeness are non-separable problems from the CEC and BBOB test suites. We select the top 5% of most representative problems to build a new test suite, providing a more representative and rigorous reference in verifying the overall performance of the optimization algorithms.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101716"},"PeriodicalIF":8.2,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049587","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

Batch subproblem coevolution with gaussian process-driven linear models for expensive multi-objective optimization 利用高斯过程驱动的线性模型进行批量子问题协同进化，实现昂贵的多目标优化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-22 DOI: 10.1016/j.swevo.2024.101700

Zhenkun Wang , Yuanyao Chen , Genghui Li , Lindong Xie , Yu Zhang

{"title":"Batch subproblem coevolution with gaussian process-driven linear models for expensive multi-objective optimization","authors":"Zhenkun Wang , Yuanyao Chen , Genghui Li , Lindong Xie , Yu Zhang","doi":"10.1016/j.swevo.2024.101700","DOIUrl":"10.1016/j.swevo.2024.101700","url":null,"abstract":"<div><p>The efficacy of surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) in addressing expensive multi-objective optimization problems (MOPs) is contingent upon the modeling techniques and model-based infill sampling strategies. In addressing this pivotal aspect, this paper introduces a pioneering methodology known as batch subproblem coevolution with Gaussian process-driven linear models (BSCo-GPLM). Specifically, from a modeling perspective, BSCo-GPLM decomposes the MOP into single-objective subproblems. Following this decomposition, for each subproblem, a Gaussian process-driven linear model (GPLM) is collaboratively trained to prevent overfitting and improve prediction accuracy. Regarding infill sampling, collaborative optimization of all GPLMs yields optimal candidate solutions for each subproblem, organized into coherent clusters. Within each cluster, only the solution with the highest utility is evaluated. Relying on the heightened prediction accuracy of the GPLM model and an efficient batch sampling strategy, BSCo-GPLM exhibits clear superiority over state-of-the-art SAMOEAs in effectively addressing expensive MOPs. The source code of BSCo-GPLM is available at <span><span>https://github.com/CIAM-Group/EvolutionaryAlgorithm_Codes/tree/main/BSCo-GPLM</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101700"},"PeriodicalIF":8.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044360","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 IGD-based prediction strategy for dynamic multi-objective optimization 基于 IGD 的动态多目标优化预测策略

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-22 DOI: 10.1016/j.swevo.2024.101713

Yaru Hu , Jiankang Peng , Junwei Ou , Yana Li , Jinhua Zheng , Juan Zou , Shouyong Jiang , Shengxiang Yang , Jun Li

{"title":"The IGD-based prediction strategy for dynamic multi-objective optimization","authors":"Yaru Hu , Jiankang Peng , Junwei Ou , Yana Li , Jinhua Zheng , Juan Zou , Shouyong Jiang , Shengxiang Yang , Jun Li","doi":"10.1016/j.swevo.2024.101713","DOIUrl":"10.1016/j.swevo.2024.101713","url":null,"abstract":"<div><p>In recent years, an increasing number of prediction-based strategies have shown promising results in handling dynamic multi-objective optimization problems (DMOPs), and prediction models are also considered to be very favorable. Nevertheless, some linear prediction models may not always be effective. In particular, when the motion direction trends of different individuals are not aligned, these models can yield inaccurate prediction results. Inverted generational distance (IGD) is a commonly used metric for evaluating the performance of algorithms. This paper proposes a prediction model based on the IGD metric. Specifically, we assume that the pareto optimal front (POF) of the population at the previous time step is the true POF, and the POF at the current time step is the approximate POF. We cluster the current population with reference to the euclidean distances from uniform points on the true POF to the current POF points, with slight overlap between adjacent clusters, enables a better tradeoff between convergence and diversity in the prediction process. We consider the movement directions of individuals within each cluster separately through different cluster distributions, while balancing the individual movement directions and the overall population movement direction by overlaying cluster coverage areas, thereby helping to avoid the clustered prediction population from getting trapped in local optima. Experimental results and comparisons with other algorithms demonstrate that this strategy exhibits strong competitiveness in handling DMOPs.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101713"},"PeriodicalIF":8.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040456","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

Self-organizing surrogate-assisted non-dominated sorting differential evolution 自组织代用辅助非支配排序差分进化论

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-20 DOI: 10.1016/j.swevo.2024.101703

Aluizio F.R. Araújo , Lucas R.C. Farias , Antônio R.C. Gonçalves

{"title":"Self-organizing surrogate-assisted non-dominated sorting differential evolution","authors":"Aluizio F.R. Araújo , Lucas R.C. Farias , Antônio R.C. Gonçalves","doi":"10.1016/j.swevo.2024.101703","DOIUrl":"10.1016/j.swevo.2024.101703","url":null,"abstract":"<div><p>Multi-objective optimization problems (MOPs) involve optimizing multiple conflicting objectives simultaneously, resulting in a set of Pareto optimal solutions. Due to the high computational or financial cost associated with evaluating fitness, expensive multi-objective optimization problems (EMOPs) further complicate the optimization process. Surrogate-assisted evolutionary algorithms (SAEAs) have emerged as a promising approach to address EMOPs by substituting costly evaluations with computationally efficient surrogate models. This paper introduces the self-organizing surrogate-assisted non-dominated sorting differential evolution (SSDE), which uses surrogate model based on a self-organizing map (SOM) to approximate the fitness function. SSDE offers advantages such as reduced computational cost, improved accuracy, and the speed of enhanced convergence. The SOM-based surrogate models effectively capture the underlying structure of the Pareto optimal set and Pareto optimal front, leading to superior approximations of the fitness function. Experimental results on benchmark functions and real-world problems, including Model-Free Adaptive Control (MFAC) and the Yagi-Uda Antenna design, demonstrate the competitiveness and efficiency of SSDE compared to other algorithms.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101703"},"PeriodicalIF":8.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012708","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

Clustering-based evolutionary algorithm for constrained multimodal multi-objective optimization 基于聚类的多模式多目标优化进化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-20 DOI: 10.1016/j.swevo.2024.101714

Guoqing Li , Weiwei Zhang , Caitong Yue , Gary G. Yen

{"title":"Clustering-based evolutionary algorithm for constrained multimodal multi-objective optimization","authors":"Guoqing Li , Weiwei Zhang , Caitong Yue , Gary G. Yen","doi":"10.1016/j.swevo.2024.101714","DOIUrl":"10.1016/j.swevo.2024.101714","url":null,"abstract":"<div><p>Handling constrained multimodal multi-objective optimization problems (CMMOPs) is a tremendous challenge as it involves the discovery of multiple equivalent constrained Pareto sets (CPSs) with the identical constrained Pareto front (CPF). However, the existing constrained multi-objective evolutionary algorithms are rarely suitable for solving CMMOPs due to the fact that they focus solely on locating CPF and do not intend to search for multiple equivalent CPSs. To address this issue, this paper proposes a framework of clustering-based constrained multimodal multi-objective evolutionary algorithm, termed FCCMMEA. In the proposed FCCMMEA, we adopt a clustering method to separate the population into multiple subpopulations for locating diverse CPSs and maintaining population diversity. Subsequently, each subpopulation evolves independently to produce offspring by an evolutionary algorithm. To balance the convergence and feasibility, we develop a quality evaluation metric in the classification strategy that considers the local convergence quality and constraint violation values, and it divides the populations into superior and inferior populations according to the quality evaluation of individuals. Furthermore, we also employ a diversity maintenance methodology in environmental selection to maintain the diverse population. The proposed FCCMMEA algorithm is compared with seven state-of-the-art competing algorithms on a standard CMMOP test suite, and the experimental results validate that the proposed FCCMMEA enables to find multiple CPSs and is suitable for handling CMMOPs. Also, the proposed FCCMMEA won the first place in the 2023 IEEE Congress on Evolutionary Computation competition on CMMOPs.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101714"},"PeriodicalIF":8.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012707","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

Failure-aware resource provisioning for hybrid computation offloading in cloud-assisted edge computing using gravity reference approach 利用重力参考方法为云辅助边缘计算中的混合计算卸载提供故障感知资源配置

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-19 DOI: 10.1016/j.swevo.2024.101704

Mustafa Ibrahim Khaleel

{"title":"Failure-aware resource provisioning for hybrid computation offloading in cloud-assisted edge computing using gravity reference approach","authors":"Mustafa Ibrahim Khaleel","doi":"10.1016/j.swevo.2024.101704","DOIUrl":"10.1016/j.swevo.2024.101704","url":null,"abstract":"<div><p>This paper tackles the challenges of computation offloading in the cloud–edge paradigm. Although many solutions exist for enhancing the server’s computational and communication efficiency, they mainly focus on reducing latency and often neglect the impact of overlapping multi-request processing on scheduling reliability. Additionally, these approaches do not account for the preemptive characteristics of applications running in the VMs that lead to higher energy consumption. We propose a novel hybrid integer multi-objective dynamic decision-making approach enhanced with the gravity reference point method. This method determines the proportion of computations executed on cloud servers versus those handled locally on edge servers. Our hybrid approach leverages the gravitational potential reference point and crowding degrees to improve the characteristics of whale populations, addressing the limitations of the traditional whale algorithm, which depends on individual whales’ varying foraging behaviors influenced by a random probability number. By evaluating the crowding level around the prey, the foraging behavior of individual whales is adjusted to enhance the algorithm’s convergence speed and optimization accuracy, thereby increasing its reliability. The results show that our hybrid computation offloading model significantly improves time latency by 76.45%, energy efficiency by 63.12%, reliability by 82%, quality of service by 83.78%, distributor throughput by 87.31%, asset availability by 73.05%, and guarantee ratio by 89.72% compared to traditional offloading methods.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101704"},"PeriodicalIF":8.2,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006851","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 constrained permutation Flowshop problem: An effective two-stage iterated greedy algorithm to minimize weighted tardiness 受约束包络流车间问题：一种有效的两阶段迭代贪婪算法，可使加权迟到时间最小化

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-18 DOI: 10.1016/j.swevo.2024.101696

Qiu-Ying Li , Quan-Ke Pan , Liang Gao , Hong-Yan Sang , Xian-Xia Zhang , Wei-Min Li

{"title":"The constrained permutation Flowshop problem: An effective two-stage iterated greedy algorithm to minimize weighted tardiness","authors":"Qiu-Ying Li , Quan-Ke Pan , Liang Gao , Hong-Yan Sang , Xian-Xia Zhang , Wei-Min Li","doi":"10.1016/j.swevo.2024.101696","DOIUrl":"10.1016/j.swevo.2024.101696","url":null,"abstract":"<div><p>In the domain of just-in-time permutation flowshop scheduling, most studies typically assume that all jobs either have their own soft due date or none of them do. However, in practice, scheduling a combination of hard and soft due date jobs, particularly with the context of emergency order insertion, remains a significant research topic. This paper addresses a constrained permutation flowshop scheduling problem with a mix of hard and soft due date jobs under total weighted tardiness criterion (CPFSP-TWT). We establish a mathematical model and propose an effective Two-Stage Iterated Greedy (ETSIG) algorithm tailored to the problem's characteristics, incorporating a two-stage constructive heuristic to generate a high-quality initial solution. We introduce problem-specific acceleration mechanisms based on position-bound considerations to enhance operational efficiency. We propose three knowledge-based repair strategies for handling infeasible solutions, along with a dynamic self-adjustment mechanism. Additionally, three efficient local search procedures integrate several specific perturbation operators to balance algorithmic exploitation and exploration abilities. Experimental evaluations affirm ETSIG's superiority over five state-of-the-art metaheuristics from closely related literature, establishing its efficacy in addressing CPFSP-TWT.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"91 ","pages":"Article 101696"},"PeriodicalIF":8.2,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142002070","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

Optimizing distributed reentrant heterogeneous hybrid flowshop batch scheduling problem: Iterative construction-local search-reconstruction algorithm 优化分布式重入异构混合流程车间批量调度问题：迭代构建-局部搜索-重构算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-08-18 DOI: 10.1016/j.swevo.2024.101681

Peng He , Biao Zhang , Chao Lu , Lei-lei Meng , Wen-qiang Zou

{"title":"Optimizing distributed reentrant heterogeneous hybrid flowshop batch scheduling problem: Iterative construction-local search-reconstruction algorithm","authors":"Peng He , Biao Zhang , Chao Lu , Lei-lei Meng , Wen-qiang Zou","doi":"10.1016/j.swevo.2024.101681","DOIUrl":"10.1016/j.swevo.2024.101681","url":null,"abstract":"<div><p>In recent years, the distributed hybrid flowshop scheduling problem (DHFSP) has garnered widespread attention due to the continuous emergence of practical challenges. The production model, characterized by multiple varieties and small batches, is widely observed in the industrial sector. Additionally, in various real-world scenarios, batches often undergo repeated processes across multiple stages. This paper addresses the research gap by introducing the reentrant nature of batches and the heterogeneity of factories into the DHFSP, resulting in a novel problem referred to as the distributed reentrant heterogeneous hybrid flowshop batch scheduling problem (DRHHFBSP). To tackle this problem, we propose a mixed-integer linear programming (MILP) model. Given that this problem falls into the NP-hard category, an iterative construction-local search-reconstruction algorithm (ICLSRA) is designed. Specifically designed by incorporating construction, local search, and reconstruction processes that have different roles, this algorithm strikes a balance between local and global search. Comparative analysis with the MILP model and state-of-the-art algorithms demonstrates the superiority of ICLSRA in achieving efficient solutions for the DRHHFBSP.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"90 ","pages":"Article 101681"},"PeriodicalIF":8.2,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006383","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