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A path relinking enhanced estimation of distribution algorithm for direct acyclic graph task scheduling problem

KNOWLEDGE-BASED SYSTEMS 2021年 228卷 107255-107255页

作者： Wu, Chu-ge Wang, Ling Wang, Jing-jing Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Superior task scheduling scheme is able to improve the performance in achieving shorter task completion time in multi-processor computing system. Large scale applications are generally modelled as direct acyclic graph (DAG) to be processed efficiently in parallel. To solve DAG task scheduling problem (DAG-SP) with the criterion of minimizing makespan, this paper proposes an estimation of distribution algorithm (EDA) enhanced by the path relinking. An efficient hybrid scheme integrating list scheduling heuristics is designed to take advantage of the knowledge of existing works. In addition, to describe the relative position relationships between the task pairs, a specific probability model is built and the task processing permutations are produced by sampling such a model. To enhance the exploitation of EDA, a path relinking based knowledge is used to design the local search method. Simulation experiments are carried out with both benchmark datasets and real-world graphs, where the comparative results show that the above designs can improve the performance effectively. Moreover, the numerical comparisons show that the proposed algorithm performs significantly better than the existing heuristics and evolutionary algorithms. (C) 2021 Elsevier B.V. All rights reserved.

关键词： estimation of distribution algorithm Direct acyclic graph task scheduling Path relinking Makespan Workflow scheduling

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A matrix cube-based estimation of distribution algorithm for the energy-efficient distributed assembly permutation flow-shop scheduling problem

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期194卷 116484-116484页

作者： Zhang, Zi-Qi Hu, Rong Qian, Bin Jin, Huai-Ping Wang, Ling Yang, Jian-Bo Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Sch Mech & Elect Engn Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Manchester Alliance Manchester Business Sch Manchester M15 6PB Lancs England

In this paper, a matrix-cube-based estimation of distribution algorithm (MCEDA) is proposed to solve the energy-efficient distributed assembly permutation flow-shop scheduling problem (EE_DAPFSP) that minimizes both the maximum completion time (C-max) and the total carbon emission (TCE) simultaneously. Firstly, a high-quality and diverse initial population is constructed via a hybrid initialization method. Secondly, a matrix-cube-based probabilistic model and its update mechanism are designed to appropriately accumulate the valuable pattern information from superior solutions. Thirdly, a suitable sampling strategy is developed to sample the probabilistic model to generate a new population per generation, so as to guide the search direction toward promising regions in solution space. Fourthly, a problem-dependent neighborhood search based on critical path is provided to perform an in-depth local search around the promising regions found by the global search. Fifthly, two types of speed adjustment strategies based on problem properties are also embedded to further improve the quality of the obtained solutions. Sixthly, the influence of the parameters is investigated based on the multi-factor analysis of variance of Design-of-Experiments. Finally, extensive experiments and comprehensive comparisons with several recent state-of-the-art multi-objective algorithms are carried out based on the well-known benchmark instances, and the statistical results demonstrate the efficiency and effectiveness of the proposed MCEDA in addressing the EE_DAPFSP.

关键词： Energy-efficient scheduling estimation of distribution algorithm Distributed flowshop scheduling Assembly line Low-carbon manufacturing

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Real-coded estimation of distribution algorithm by Using Probabilistic Models with Multiple Learning Rates

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Procedia Computer Science 2011年 4卷 1244-1251页

作者： Masahiro Nakao Tomoyuki Hiroyasu Mitsunori Miki Hisatake Yokouchi Masato Yoshimi Center for Computational Sciences University of Tsukuba Ibaraki Japan Department of Life and Medical Sciences Doshisha University Kyoto Japan Faculty of Science and Engineering Doshisha University Kyoto Japan

Here, a new Real-coded estimation of distribution algorithm (EDA) is proposed. The proposed EDA is called Real-coded EDA using Multiple Probabilistic Models (RMM). RMM includes multiple types of probabilistic models with different learning rates and diversities. The search capability of RMM was examined through several types of continuous test function. The results indicated that the search capability of RMM is better than or equivalent to that of existing Real-coded EDAs. Since better searching points are distributed for other probabilistic models positively, RMM can discover the global optimum in the early stages of the search.

关键词： estimation of distribution algorithm optimization continuous function

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Effective estimation of distribution algorithm for Stochastic Job Shop Scheduling Problem

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Procedia Computer Science 2013年 20卷 102-107页

作者： Xinchang Hao Lin Lin Mitsuo Gen Katsuhisa Ohno Waseda University Kita-Kyushu Japan Dalian University of Technology Dalian China Fuzzy Logic Systems Institute Kitakyushu Japan National Tsing Hua University Taiwan Aichi Institute of Technology Nagoya Japan

This paper propose an effective estimation of distribution algorithm (EDA), which solves the stochastic job-shop scheduling problem (S-JSP) with the uncertainty of processing time, to minimize the expected average makespan within a reasonable amount of calculation time. With the framework of proposed EDA, the probability model of operation sequence is estimated firstly. For sampling the processing time of each operation with the Monte Carlo methods, we use allocation method to decide the operation sequence then the expected makespan of each sampling is evaluated. Subsequently, updating mechanism of the probability models is proposed with the best solutions to obtain. Finally, for comparing with some existing algorithms by numerical experiments on the benchmark problems, we demonstrate the proposed effective estimation of distribution algorithm can obtain acceptable solution in the aspects of schedule quality and computational efficiency.

关键词： estimation of distribution algorithm Manufacturing Management Stochastic Job Shop Scheduling Problem

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A hybrid estimation of distribution algorithm for distributed flexible job shop scheduling with crane transportations

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 62卷

作者： Du, Yu Li, Jun-qing Luo, Chao Meng, Lei-lei Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China Liaocheng Univ Sch Comp Sci & Technol Liaocheng 252059 Shandong Peoples R China

Distributed flexible job shop scheduling has attracted research interest due to the development of global man-ufacturing. However, constraints including crane transportation and energy consumption should be considered with the realistic requirements. To address this issue, first, we modeled the problem by utilizing an integer programming method, wherein the makespan and energy consumptions during the machine process and crane transportation are optimized simultaneously. Afterward, a hybrid algorithm consisting of estimation of distri-bution algorithm (EDA) and variable neighborhood search (VNS) was proposed to solve the problem, where an identification rule of four crane conditions was designed to make fitness calculation feasible. In EDA compo-nent, the parameters in probability matrices are set to be self-adaptive for stable convergence to obtain better output. Moreover, a probability mechanism was applied to control the activity of the EDA component. In VNS component, five problem-specific neighborhood structures including global and local strategies are employed to enhance exploitation ability. The simulation tests results confirmed that the proposed hybrid EDA-VNS algo-rithm can solve the considered problem with high efficiency compared with other competitive algorithms, and the proposed improving strategies are verified to have significance in better performance.

关键词： Distributed flexible job shop scheduling Crane transportation estimation of distribution algorithm Variable neighborhood search Multi-objective optimization

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An Augmented estimation of distribution algorithm For Multi-Carpooling Problem With Time Window

An Augmented Estimation of Distribution Algorithm For Multi-...

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IEEE Vehicular Technology Conference

作者： Fang Zhang Z. J. Yang Y. Wang Fangjun Kuang School of Computer and Communication Engineering Liaoning Shihua University Lab. of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences University of Stuttgart

ISBN: (纸本)9781509016990

A multi-carpooling model is proposed for the multi-vehicle carpooling problem in distributed parallel computing environment. A two-stage stochastic optimization of the estimation of distribution algorithm solves the optimum of the multi-carpooling problem with a carpooling probabilistic matrix. A ridable matrix initiates the carpooling probabilistic matrix, and the carpooling probabilistic matrix continues updating during the optimization. The carpooling model mines efficient and compromised ridesharing routes for shared riders by the optimization iterations. Experimental results indicate that the carpooling model has the characteristics of effective and efficient traffic including shorter waiting time, more passenger load, and less average riding distance.

关键词： Carpooling problem Pickup and delivery problem estimation of distribution algorithm Stochastic optimization Allocation algorithm CARPOOLING delivery problem Time windows

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A New Fuzzy Classification Method Based estimation of distribution algorithm

A New Fuzzy Classification Method Based Estimation of Distri...

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2012 International Conference on Convergence Computer Technology(IC3T 2012)

作者： Gao Xiao-xia Huo Wei-gang The College of computer science and technology Civil Aviation University of China

In order to improve fuzzy classification model's accuracy and interpretability,a fuzzy classification method based on estimation of distribution algorithm was *** first constructs initial fuzzy rule set using Apriori principle in the field of data mining,then builds fuzzy classification model by extracting rule from initial fuzzy rule set automatically through Pittsburghstyle binary coding method and UMDA(Univariate Marginal distribution algorithm) estimation of distribution *** experiment on benchmark datasets show that the proposed approach has better performance than fuzzy classification model based on genetic algorithm

关键词： Fuzzy classification estimation of distribution algorithm UMDA Fuzzy rule

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An estimation of distribution algorithm with Efficient Constructive Repair/Improvement Operator for the Dynamic Weapon-Target Assignment

An Estimation of Distribution Algorithm with Efficient Const...

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第三十一届中国控制会议

作者： XIN Bin~(1,2),CHEN Jie~(1,3) 1.School of Automation,Beijing Institute of Technology,Beijing 100081 2.Decision and Cognitive Sciences Research Centre,Manchester Business School,University of Manchester,Manchester M15 6PB 3.Key Laboratory of Complex System Intelligent Control and Decision,Ministry of Education,Beijing 100081

This paper reports our recent research about new efficient problem-solvers for the dynamic weapon-target assignment (DWTA).A binary-encoding-based estimation of distribution algorithm(EDA) is proposed to solve DWTA *** elaborate constructive repair/improvement(CRI) operator is proposed and integrated into the EDA to achieve constraint saturation,which conduces to constraint satisfaction as well as the improvement of generated *** performance comparison against another two EDAs which employ well-known constraint handling methods demonstrates the superiority of the CRI *** proposed EDA based on the CRI operator also shows very competitive and even better performance against several state-of-the-art DWTA algorithms.

关键词： Dynamic weapon-target assignment estimation of distribution algorithm constructive repair/improvement operator constraint handling

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An estimation of distribution algorithm for resource-constrained project scheduling problem

An estimation of distribution algorithm for resource-constra...

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2010 Chinese Control and Decision Conference

作者： Chen Fang,Ling Wang,Ye Xu TNList,Department of Automation,Tsinghua University,Beijing,100084

An estimation of distribution algorithm(EDA) is proposed to solve resource-constrained project scheduling problem(RCPSP).In the EDA,individual is encoded based on the extended active list,and a probability model of the distribution for each activity in a project and its updating mechanism are *** algorithm determines the initial probability matrix according to an initial set of solutions generated by the regret-based sampling method and priority rule,and decodes the individuals by using serial schedule generation ***,a permutation based local search method is incorporated into the algorithm to enhance the exploitation ability so as to further improve the searching *** results based on benchmarks and comparisons with some existing algorithms demonstrate the feasibility and effectiveness of our proposed EDA.

关键词： estimation of distribution algorithm Resource-constrained project scheduling Probability model

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Histogram-Based estimation of distribution algorithm with RPCL Clustering in Continuous Domain

Histogram-Based Estimation of Distribution Algorithm with RP...

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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： Hong WU Wei-ping WANG School of Information System and Management National University of Defense Technology Chang Sha P.R.China

Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. Nowadays, histogram probabilistic model has become a hot topic in the field of estimation of distribution algorithms because of its intrinsic multimodality that makes it proper to describe the solution distribution of complex and multimodal continuous problems. To make histogram probabilistic model more efficiently explore and exploit the search space, rival penalized competitive learning (RPCL) clustering was brought into the algorithm, so that the algorithm could use the knowledge about distribution of values belong to each span. Experimental results showed that the improved algorithm in this paper can give comparable with or better performance than those improved algorithms.

关键词： estimation of distribution algorithm histogram probabilistic model RPCL clustering global optimum elitist strategy

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