Swarm and Evolutionary Computation最新文献_第9页

A hierarchical reinforcement learning-aware hyper-heuristic algorithm with fitness landscape analysis 分层强化学习感知超启发式算法与适应性景观分析

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-20 DOI: 10.1016/j.swevo.2024.101669

{"title":"A hierarchical reinforcement learning-aware hyper-heuristic algorithm with fitness landscape analysis","authors":"","doi":"10.1016/j.swevo.2024.101669","DOIUrl":"10.1016/j.swevo.2024.101669","url":null,"abstract":"<div><p>The automation of meta-heuristic algorithm configuration holds the utmost significance in evolutionary computation. A hierarchical reinforcement learning-aware hyper-heuristic algorithm with fitness landscape analysis (HRLHH) is proposed to flexibly configure the suitable algorithms under various optimization scenarios. Two kinds of fitness landscape analysis techniques improved based on specific problem characteristics construct the state spaces for hierarchical reinforcement learning. Among them, an adaptive classification based on dynamic ruggedness of information entropy is designed to discern the complexity of problems, which serves as the basis for decision-making actions in upper-layer space. Additionally, an online dispersion metric based on knowledge is further presented to distinguish the precise landscape features in lower-layer space. In light of the characteristics of the state spaces, the hierarchical action spaces composed of meta-heuristics with disparate exploration and exploitation are designed, and various action selection strategies are introduced. Taking into account the real-time environment and algorithm evolution behavior, dynamic reward mechanisms based on evolutionary success rate and population convergence rate are utilized to enhance search efficiency. The experimental results on the IEEE Congress on Evolutionary Computation (CEC) 2017, CEC 2014, and large-scale CEC 2013 test suites demonstrate that the proposed HRLHH exhibits superiority in terms of accuracy, stability, and convergence speed, and possesses strong generalization.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141732135","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

Large-scale power system multi-area economic dispatch considering valve point effects with comprehensive learning differential evolution 利用综合学习差分进化考虑阀点效应的大规模电力系统多区域经济调度

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-18 DOI: 10.1016/j.swevo.2024.101620

{"title":"Large-scale power system multi-area economic dispatch considering valve point effects with comprehensive learning differential evolution","authors":"","doi":"10.1016/j.swevo.2024.101620","DOIUrl":"10.1016/j.swevo.2024.101620","url":null,"abstract":"<div><p>The role of multi-area economic dispatch (MAED) in power system operation is increasingly significant. It is a non-linear and multi-constraint problem with many local extremes when considering the valve point effects, posing challenges in obtaining a globally optimal solution, especially for large-scale systems. In this study, an improved variant of differential evolution (DE) called CLDE based on comprehensive learning strategy (CLS) is proposed to solve this problem. Three improved strategies are employed to enhance the performance of CLDE. (1) A CLS-based guided mutation strategy is proposed, in which learning exemplars constructed by competent individuals are used to generate mutant vectors to prevent the searching away from global optimum and speed up convergence. (2) A time-varying increasing crossover rate is devised. It can endow CLDE with a larger probability at the later stage to help individuals escape from local extremes. (3) A CLS-based crossover strategy is presented. Trial vectors directly utilize the information from learning exemplars for evolving, which can ensure the search efficiency and population diversity. CLDE is applied to six MAED cases. Compared with DE, it approximately consumes 32 %, 35 %, 10 %, 22 %, 62 %, and 20 % of evaluations to attain comparable results, saves 126.2544$/h, 81.8173$/h, 152.0660$/h, 360.7907$/h, 65.5757$/h, and 1732.8544$/h in fuel costs on average, and exhibits improvements of 34.77 %, 1.80 %, 0.00 %, 76.09 %, 95.15 %, and 16.76 % in robustness, respectively. Moreover, it also outperforms other state-of-the-art algorithms significantly in statistical analysis. Furthermore, the effects of improved strategies on CLDE are thoroughly investigated.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729058","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 Q-Learning based NSGA-II for dynamic flexible job shop scheduling with limited transportation resources 基于 Q-Learning 的 NSGA-II，适用于运输资源有限的动态灵活作业车间调度

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-18 DOI: 10.1016/j.swevo.2024.101658

{"title":"A Q-Learning based NSGA-II for dynamic flexible job shop scheduling with limited transportation resources","authors":"","doi":"10.1016/j.swevo.2024.101658","DOIUrl":"10.1016/j.swevo.2024.101658","url":null,"abstract":"<div><p>With the widespread adoption of intelligent transportation equipment such as AGVs in the manufacturing field, the flexible job shop scheduling considering limited transportation resources has increasingly attracted attention. However, current research does not consider various dynamic disturbances in real production scenarios, resulting in lower executability of scheduling solutions. To solve this problem, a dynamic flexible job shop scheduling model with limited transportation resources is established, aiming to minimize makespan and total energy consumption. Considering three types of disturbances: job cancellation, machine breakdown, and AGV breakdown, the corresponding event-driven rescheduling strategy is proposed, and a rescheduling instability index is designed to measure the performance of the rescheduling strategy. A <span><math><mi>Q</mi></math></span>-Learning-based NSGA-II algorithm (QNSGA-II) is proposed. By learning the feedback historical search experience, it adaptively selects the appropriate neighborhood structures for local search; and a hybrid initialization strategy tailored to the problem characteristics is designed to improve the optimization performance of the algorithm. Through simulation experiments, the effectiveness of the rescheduling strategies and the superiority of the QNSGA-II algorithm in solving such problems are validated.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639108","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 double deep Q-network algorithm considering external non-dominant set for multi-objective dynamic flexible job shop scheduling problems 考虑外部非优势集的多目标动态灵活作业车间调度问题的两阶段双深 Q 网络算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-18 DOI: 10.1016/j.swevo.2024.101660

{"title":"Two-stage double deep Q-network algorithm considering external non-dominant set for multi-objective dynamic flexible job shop scheduling problems","authors":"","doi":"10.1016/j.swevo.2024.101660","DOIUrl":"10.1016/j.swevo.2024.101660","url":null,"abstract":"<div><p>With the continuous advancement of the manufacturing industry, many random disturbances gradually appear in the job shop production process, such as the insertion of new jobs or random machine failures. This type of disruption often creates production chaos and scheduling problems. This paper takes new job insertion and random machine failure as dynamic events. It establishes an optimization model for dynamic flexible job shop scheduling (DFJSP) problems to cope with this issue. A two-stage double deep Q-network, TS-DDQN algorithm is proposed based on an improved deep reinforcement learning algorithm to solve the complex multi-objective DFJSP optimization problem by adopting two-stage decision-making. In the TS-DDQN, an external non-dominated set is used to enhance the training speed of the network and the quality of the solution, which can store the non-dominated solutions. Moreover, the solutions on the Pareto front are used to train the network parameters. Extensive experimentation is conducted on benchmark datasets to evaluate the performance of the proposed algorithm against the existing scheduling methods. The outcomes underscore the superior efficacy of the proposed algorithm concerning solution quality, convergence speed, and adaptability within dynamic environments. This research contributes to advancing the methods in solving multi-objective DFJSP problems and highlights the potential of deep reinforcement learning to yield managerial advantages in manufacturing industries.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639107","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 differential evolution with dynamic population assignment for constrained multi-objective optimization 针对受限多目标优化的两阶段差分进化与动态种群分配

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-16 DOI: 10.1016/j.swevo.2024.101657

{"title":"Two-stage differential evolution with dynamic population assignment for constrained multi-objective optimization","authors":"","doi":"10.1016/j.swevo.2024.101657","DOIUrl":"10.1016/j.swevo.2024.101657","url":null,"abstract":"<div><p>Using infeasible information to balance objective optimization and constraint satisfaction is a very promising research direction to address constrained multi-objective problems (CMOPs) via evolutionary algorithms (EAs). The existing constrained multi-objective evolutionary algorithms (CMOEAs) still face the issue of striking a good balance when solving CMOPs with diverse characteristics. To alleviate this issue, in this paper we develop a two-stage different evolution with a dynamic population assignment strategy for CMOPs. In this approach, two cooperative populations are used to provide feasible driving forces and infeasible guiding knowledge. To adequately utilize the infeasibility information, a dynamic population assignment model is employed to determine the primary population, which is used as the parents to generate offspring. The entire search process is divided into two stages, in which the two populations work in weak and strong cooperative ways, respectively. Furthermore, multistrategy-based differential evolution operators are adopted to create aggressive offspring. The superior exploration and exploitation ability of the proposed algorithm is validated via some state-of-the-art CMOEAs over artificial benchmarks and real-world problems. The experimental results show that our proposed algorithm gained a better, or more competitive, performance than the other competitors, and it is an effective approach to balancing objective optimization and constraint satisfaction.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141630710","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 feedforward neural networks using a modified weighted mean of vectors: Case study chemical datasets 使用修改后的向量加权平均值优化前馈神经网络：化学数据集案例研究

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-13 DOI: 10.1016/j.swevo.2024.101656

Essam H. Houssein , Mosa E. Hosney , Marwa M. Emam , Diego Oliva , Eman M.G. Younis , Abdelmgeid A. Ali , Waleed M. Mohamed

{"title":"Optimizing feedforward neural networks using a modified weighted mean of vectors: Case study chemical datasets","authors":"Essam H. Houssein , Mosa E. Hosney , Marwa M. Emam , Diego Oliva , Eman M.G. Younis , Abdelmgeid A. Ali , Waleed M. Mohamed","doi":"10.1016/j.swevo.2024.101656","DOIUrl":"https://doi.org/10.1016/j.swevo.2024.101656","url":null,"abstract":"<div><p>This paper proposes a modified version of the weighted mean of vectors algorithm (mINFO), which combines the strengths of the INFO algorithm with the Enhanced Solution Quality Operator (ESQ). The ESQ boosts the quality of the solutions by avoiding optimal local values, verifying that each solution moves towards a better position, and increasing the convergence speed. Furthermore, we employ the mINFO algorithm to optimize the connection weights and biases of feedforward neural networks (FNNs) to improve their accuracy. The efficacy of FNNs for classification tasks is mainly dependent on hyperparameter tuning, such as the number of layers and nodes. The mINFO was evaluated using the IEEE Congress on Evolutionary Computation held in 2020 (CEC’2020) for optimization tests, and ten chemical data sets were applied to validate the performance of the FNNs classifier. The proposed algorithm’s results have been evaluated with those of other well-known optimization methods, including Runge Kutta optimizer’s (RUN), particle swarm optimization (PSO), grey wolf optimization (GWO), Harris hawks optimization (HHO), whale optimization algorithm (WOA), slime mould algorithm (SMA) and the standard weighted mean of vectors (INFO). In addition, some improved metaheuristic algorithms. The experimental results indicate that the proposed mINFO algorithm can improve the convergence speed and generate effective search results without increasing computational costs. In addition, it has improved the FNN’s classification efficiency.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606219","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 particle swarm optimization-based deep clustering algorithm for power load curve analysis 基于粒子群优化的电力负荷曲线分析深度聚类算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-13 DOI: 10.1016/j.swevo.2024.101650

Li Wang , Yumeng Yang , Lili Xu , Ziyu Ren , Shurui Fan , Yong Zhang

引用次数: 0

Multi-objective optimization algorithm for multi-workflow computation offloading in resource-limited IIoT 针对资源有限的物联网多工作流计算卸载的多目标优化算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-11 DOI: 10.1016/j.swevo.2024.101646

Bao-Shan Sun, Hao Huang, Zheng-Yi Chai, Ying-Jie Zhao, Hong-Shen Kang

{"title":"Multi-objective optimization algorithm for multi-workflow computation offloading in resource-limited IIoT","authors":"Bao-Shan Sun, Hao Huang, Zheng-Yi Chai, Ying-Jie Zhao, Hong-Shen Kang","doi":"10.1016/j.swevo.2024.101646","DOIUrl":"https://doi.org/10.1016/j.swevo.2024.101646","url":null,"abstract":"<div><p>Industrial internet of things (IIoT) connects traditional industrial devices with the network to provide intelligent services, which is regarded as the key technology for achieving Industry 4.0 and enabling the transformation of the manufacturing sector. Multi-access edge computing (MEC) has brought significant opportunities to expedite the development of IIoT. However, the unique task characteristics and dense deployment of IIoT devices, coupled with the resource starvation problem (RSP) arising from the limited resources of edge servers, pose challenges to the direct applicability of existing MEC algorithms in MEC-assisted IIoT scenarios. To this end, a multi-objective evolutionary algorithm is proposed to simultaneously optimize delay and energy consumption for multi-workflow execution in resource-limited IIoT. First, the initialization of execution location based on delay and the initialization of execution order satisfying the priority constraint can generate high-quality initial solutions. Then, the improved crossover and mutation operations guide the population evolution, which can span the large infeasible solution region. Finally, dynamic task scheduling (DTS) dynamically changes the execution location of tasks affected by RSP according to the execution efficiency, so as to avoid the tasks blindly waiting for server resources. The comprehensive simulation results demonstrate the effectiveness of the proposed method in achieving a balance between the execution delay and energy consumption of IIoT devices.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141595048","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

BDE-Jaya: A binary discrete enhanced Jaya algorithm for multiple automated guided vehicle scheduling problem in matrix manufacturing workshop BDE-Jaya：矩阵制造车间多辆自动导航车调度问题的二元离散增强型 Jaya 算法

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-11 DOI: 10.1016/j.swevo.2024.101651

Hao Chi, Hong-Yan Sang, Biao Zhang, Peng Duan, Wen-Qiang Zou

{"title":"BDE-Jaya: A binary discrete enhanced Jaya algorithm for multiple automated guided vehicle scheduling problem in matrix manufacturing workshop","authors":"Hao Chi, Hong-Yan Sang, Biao Zhang, Peng Duan, Wen-Qiang Zou","doi":"10.1016/j.swevo.2024.101651","DOIUrl":"https://doi.org/10.1016/j.swevo.2024.101651","url":null,"abstract":"<div><p>With the advent of \"Industry 4.0\", more matrix manufacturing workshops have adopted automated guided vehicle (AGV) for material handling. AGV transportation has become a key link in manufacturing production. Traditional AGVs scheduling problem (AGVSP) is studied in depth. However, most research overlooks an important problem, in production with limited resources, the number of AGVs is insufficient. Therefore, the wait time of workstations is longer than expected. The service time of the task is delayed and the cost is increased. To solve above problem, this paper proposes the binary discrete enhanced Jaya (BDE-Jaya) algorithm. The main goal is to minimize transportation cost, including AGV traveling cost, service early penalty, and total tardiness (TTD). A key-task shift method is proposed to reduce TTD and task service early penalty. Two heuristics based on the problem features are designed to generate the initial solution. In the evolutionary stage, three offspring generation methods are used to improve the algorithm exploitation capability and exploration capability. Then, an insertion-based repair method is designed to prevent the exploitation process falling into local optimum. Furthermore, three parameters are proposed to improve the performance of the algorithm. Finally, simulation experiment shows that the proposed BDE-Jaya algorithm has significant advantages compared with other algorithms.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141595047","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

Balancing exploration and exploitation in dynamic constrained multimodal multi-objective co-evolutionary algorithm 平衡动态约束多模式多目标协同进化算法中的探索与开发

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2024-07-11 DOI: 10.1016/j.swevo.2024.101652

Guoqing Li , Weiwei Zhang , Caitong Yue , Yirui Wang

{"title":"Balancing exploration and exploitation in dynamic constrained multimodal multi-objective co-evolutionary algorithm","authors":"Guoqing Li , Weiwei Zhang , Caitong Yue , Yirui Wang","doi":"10.1016/j.swevo.2024.101652","DOIUrl":"https://doi.org/10.1016/j.swevo.2024.101652","url":null,"abstract":"<div><p>Constrained multimodal multi-objective optimization (CMMOPs) involves multiple equivalent constrained Pareto optimal sets (CPSs) matching the same constrained Pareto front (CPF). An essential challenge in solving CMMOPs is how to balance exploration and exploitation in searching for the CPSs. To tackle this issue, a dynamic constrained co-evolutionary multimodal multi-objective algorithm termed DCMMEA is developed in this paper. DCMMEA involves a constraint-relaxed population for handling constraints and a convergence-relaxed population for improving convergence quality. Subsequently, a constraint-relaxed epsilon strategy that considers the constraint violation degree between individuals is designed and applied dynamically in the constraint-relaxed population to develop equivalent CPSs. Similarly, a dynamic convergence-relaxed epsilon strategy that considers the differences between objective values is developed and used dynamically in the convergence-relaxed population. It explores CPSs with high convergence quality and transfers the convergence knowledge to the constraint-relaxed population. Additionally, the constraint- relaxed population size is dynamically increased and the convergence-relaxed population size is dynamically decreased to balance the exploration and exploitation procedures. Experiments are performed on standard CMMOP test suites and validate that DCMMEA obtains superior performance on solving CMMOPs in comparison to state-of-the-art algorithms. Also, DCMMEA is implemented on standard CMOPs and demonstrated good performance in handling CMOPs.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141595020","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