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Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm 认领 引用 被引量：15
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作者 WANG Cuiyu LI Yang LI Xinyu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2021年第2期261-271,共11页
The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborativ... The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms. 展开更多
关键词 flexible job shop scheduling problem(FJSP) collaborative genetic algorithm co-evolutionary algorithm
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Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems 认领 引用 被引量：3
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作者 YAO Yu ZHU Shanfeng CHEN Xinmeng 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第5期1086-1090,共5页
In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of consider... In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage. 展开更多
关键词 Kendall correlation collaborative filtering algorithms recommender systems positive correlation
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Design and Implementation of Collaborative Filtering Recommendation Algorithm for Multi-layer Networks 认领 引用
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作者 Ling Gou Lin Zhou Yuzhi Xiao 《国际计算机前沿大会会议论文集》 EI 2021年第1期32-50,共19页
With the continuous development of mobile communications and Internet technologies,the marketing model of the communications industry has shifted from calling-based to social APP-based personalized recommendations.In ... With the continuous development of mobile communications and Internet technologies,the marketing model of the communications industry has shifted from calling-based to social APP-based personalized recommendations.In order to improve the accuracy of recommendation,this paper proposes a recommendation algorithm for social analysis.Empirical data was firstly used to construct a“user-APP”two-layer communication network model,and then the traditional collaborative filtering recommendation technology was integrated to reconstruct similar users and similar APP network model.The bipartite graph weight distribution method was taken to recommend targets in the obtained network model.The experimental simulation shows that,in view of the characteristics of the twolayer communication network,compared with the traditional recommendation algorithm,the algorithm effectively improves the accuracy of the score prediction. 展开更多
关键词 Two-layer communication network Social network analysis Recommendation algorithm Collaborative filtering algorithm
Combat task-oriented weapon portfolio selection method 认领 引用
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作者 ZHU Renqi DAI Yulong +3 位作者 DONG Yijun LI Jiaqing ZHANG Nannan QIU Zhiran 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期921-932,共12页
Existing weapon portfolio selection methods do not sufficiently support specific combat tasks,with uncertainty in the decision information.Therefore,a combat task–oriented weapon portfolio selection method that adapt... Existing weapon portfolio selection methods do not sufficiently support specific combat tasks,with uncertainty in the decision information.Therefore,a combat task–oriented weapon portfolio selection method that adapts weapon capabilities to combat tasks is proposed.The approach is based on specific combat tasks and weapon background,using fuzzy interval values to describe indicators and the applicability of a weapon portfolio.In addition,an interval entropy weighting method is applied to obtain weight information of indicators.Meanwhile,we define the similarity measure of fuzzy interval values and use the interval fuzzy collaborative filtering algorithm to calculate the fitness of the residual weapons.Furthermore,the interval fuzzy set clustering algorithm clusters the tasks to inform the decision of weapon portfolio.Finally,we verify the method's feasibility and advancement by comparing actual combat tasks as examples with the traditional methods.The contributions of this paper include improvements to the accuracy and reliability of decisionmaking from the perspective of adapting weapon capability to combat tasks.At the same time,this paper accounts for the method's shortcomings by considering the hesitancy and ambiguity of the indicator data. 展开更多
关键词 weapon portfolio selection interval-value hesitation fuzzy theory collaborative filtering algorithm entropy weighting method
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Design of Hybrid Recommendation Algorithm in Online Shopping System 认领 引用 被引量：1
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作者 Yingchao Wang Yuanhao Zhu +2 位作者 Zongtian Zhang Huihuang Liu Peng Guo 《Journal of New Media》 2021年第4期119-128,共10页
In order to improve user satisfaction and loyalty on e-commerce websites,recommendation algorithms are used to recommend products that may be of interest to users.Therefore,the accuracy of the recommendation algorithm... In order to improve user satisfaction and loyalty on e-commerce websites,recommendation algorithms are used to recommend products that may be of interest to users.Therefore,the accuracy of the recommendation algorithm is a primary issue.So far,there are three mainstream recommendation algorithms,content-based recommendation algorithms,collaborative filtering algorithms and hybrid recommendation algorithms.Content-based recommendation algorithms and collaborative filtering algorithms have their own shortcomings.The content-based recommendation algorithm has the problem of the diversity of recommended items,while the collaborative filtering algorithm has the problem of data sparsity and scalability.On the basis of these two algorithms,the hybrid recommendation algorithm learns from each other’s strengths and combines the advantages of the two algorithms to provide people with better services.This article will focus on the use of a content-based recommendation algorithm to mine the user’s existing interests,and then combine the collaborative filtering algorithm to establish a potential interest model,mix the existing and potential interests,and calculate with the candidate search content set.The similarity gets the recommendation list. 展开更多
关键词 Recommendation algorithm hybrid recommendation algorithm content-based recommendation algorithm collaborative filtering algorithm
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Mathematical Modeling and a Multiswarm Collaborative Optimization Algorithm for Fuzzy Integrated Process Planning and Scheduling Problem 认领 引用 被引量：2
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作者 Qihao Liu Cuiyu Wang +1 位作者 Xinyu Li Liang Gao 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第2期285-304,共20页
Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the... Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem. 展开更多
关键词 Integrated Process Planning and Scheduling(IPPS) fuzzy processing time fuzzy completion time MultiSwarm Collaborative Optimization Algorithm(MSCOA)
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A New Filter Collaborative State Transition Algorithm for Two-Objective Dynamic Reactive Power Optimization 认领 引用 被引量：5
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作者 Hongli Zhang Cong Wang Wenhui Fan 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2019年第1期30-43,共14页
Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and s... Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and switching operation constraints, DRPO is formulated as a nonlinear constrained two-objective optimization problem in this paper. The first objective is to minimize the real power loss and the Total Voltage Deviations(TVDs), and the second objective is to minimize incremental system loss. Then a Filter Collaborative State Transition Algorithm(FCSTA) is presented for solving DRPO problems. Two populations corresponding to two different objectives are employed. Moreover, the filter technique is utilized to deal with constraints. Finally, the effectiveness of the proposed method is demonstrated through the results obtained for a 24-hour test on Ward & Hale 6 bus, IEEE 14 bus, and IEEE 30 bus test power systems. To substantiate the effectiveness of the proposed algorithms, the obtained results are compared with different approaches in the literature. 展开更多
关键词 dynamic reactive power optimization filter collaborative state transition algorithm Ward & Hale 6 bus IEEE 14 bus IEEE 30 bus
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Prediction of(n,2n)reaction cross-sections of long-lived fission products based on tensor model 认领 引用 被引量：1
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作者 Jia-Li Huang Hui Wang +7 位作者 Ying-Ge Huang Er-Xi Xiao Yu-Jie Feng Xin Lei Fu-Chang Gu Long Zhu Yong-Jing Chen Jun Su 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2024年第10期208-221,共14页
Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reac... Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reaction cross-section of long-lived fission products based on a tensor model.This tensor model is an extension of the collaborative filtering algorithm used for nuclear data.It is based on tensor decomposition and completion to predict(n,2n)reaction cross-sections;the corresponding EXFOR data are applied as training data.The reliability of the proposed tensor model was validated by comparing the calculations with data from EXFOR and different databases.Predictions were made for long-lived fission products such as60Co,79Se,93Zr,107P,126Sn,and137Cs,which provide a predicted energy range to effectively transmute long-lived fission products into shorter-lived or less radioactive isotopes.This method could be a powerful tool for completing(n,2n)reaction cross-sectional data and shows the possibility of selective transmutation of nuclear waste. 展开更多
关键词 (n,2n)Reaction cross-section Tensor model Machine learning Collaborative filtering algorithm Selective transmutation
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