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2nd Asia Conference on algorithms, Computing and Machine Learning (CACML)

作者： Gao, Yuan Y. G. Zheng, Chengfeng C. Z. Chen, Ju J. C. Guo, Yueling Y. G. Chengdu Univ Tradit Chinese Med Sch Med Informat Engn Chengdu 611137 Peoples R China Univ Sains Malaysia Sch Math Sci Minden 11800 Penang Malaysia

ISBN: (纸本)9781450399449

The Discrete Hopfield Neural Network introduces a G-Type Random 3 Satisfiability logic structure, which can improve the flexibility of the logic structure and meet the requirements of all combinatorial problems. Usually, Exhaustive Search (ES) is regarded as the basic learning algorithm to search the fitness of neurons. To improve the efficiency of the learning algorithm. In this paper, we introduce the estimation of distribution algorithm (EDA) as a learning algorithm for the model. To study the learning mechanism of EDA to improve search efficiency, this study focuses on the impact of EDA on the model under different proportions of literals and evaluates the performance of the model at different phases through evaluation indicators. Analyze the effect of EDA on the synaptic weights and the global solution. From the discussion, it can be found that compared with ES, EDA has a larger search space at the same efficiency, which makes the probability of obtaining satisfactory weights higher, and the proportion of global solutions obtained is higher. Higher proportions of positive literals help to improve the model performance.

关键词： Hopfield Neural Network Exhaustive Search estimation of distribution algorithm Meta-heuristic

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estimation of distribution algorithm using Variety of Information

Estimation of Distribution Algorithm using Variety of Inform...

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3rd International Workshop on Evidential Assessment of Software Technologies (EAST)

作者： Yu, Juan He, Yuyao Northwestern Polytech Univ Coll Marine Xian 710072 Peoples R China

ISBN: (纸本)9781450329651

Former information of probability model and inferior individuals were discarded in the research of estimation of distribution algorithm usually, but they may contain useful information. In this paper, the former probability information is introduced to avoid premature convergence caused by continuously select superior individuals of current population tobuilt probability model, and the individual sampling from superior probability model is filtered by inferior probability model to avoid generating inferior individuals. The algorithm is simulated through the widely used knapsack examples, the results verify the validity of the proposed method, and give suggestion for the choice of parameter through simulation and analysis.

关键词： estimation of distribution algorithm probability model former information of probability model inferior individuals

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estimation of distribution algorithm Based Gas Scheduling Method

Estimation of Distribution Algorithm Based Gas Scheduling Me...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Li, Na Li, Li Zhu, Jun Wei, Na Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9781479925186;9781479925193

An estimation of distribution algorithm based gas scheduling method is proposed to optimize gas utilization with less emission. Firstly, gas energy flow network (G-EFN) is analyzed, the mathematical model of the gas scheduling is presented. Secondly, the process priority related to gas distribution is given, and a two-stage gas scheduling method is proposed. The major concern of the first stage is to satisfy basic energy requirement of each process. The second stage is to optimize the gas distribution among the processes by using an improved estimation of distribution algorithm to obtain efficient gas utilization with less emission. Finally, a typical iron and steel enterprise is taken as an example to support the argument. The simulation results show that the proposed method can decrease the gas emission to a significant level.

关键词： gas energy flow network estimation of distribution algorithm two-stage gas scheduling

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estimation of distribution algorithm with machine learning for permutation flowshop scheduling with total tardiness criterion

Estimation of distribution algorithm with machine learning f...

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作者： Hao, Liying Li, Tieke Wang, Bailin Luan, Zhiwei Donlinks School of Economics and Management University of Science and Technology Beijing Beijing China Engineering Research Center of MES Technology for Iron and Steel Production Ministry of Education Beijing China Beijing China

ISBN: (纸本)9781538636749

This paper studies a permutation flowshop scheduling problem (PFSP) with the objective of total tardiness minimization. An improved estimation of distribution algorithm with machine learning, named ML-EDA, is proposed. This algorithm divides the job permutation into several segments and introduces an external archive to keep elite solutions. A two-layer probability model is then constructed in the ML-EDA, and the statistical learning method is employed to produce the probability that the each of job falls at each segment and the probability that the job falls at each location in the segment. Computational results based on benchmark illustrated that the ML-EDA can obtain better solution than the standard EDA for the permutation flowshop scheduling problem with the objective of minimizing total tardiness. © 2017 IEEE.

关键词： estimation of distribution algorithm Machine-learning Permutation flowshop scheduling problem Total tardiness

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estimation of distribution algorithm with machine learning for permutation flowshop scheduling with total tardiness criterion

Estimation of distribution algorithm with machine learning f...

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10th International Symposium on Computational Intelligence and Design (ISCID)

作者： Hao Liying Li Tieke Wang Bailin Luan Zhiwei Univ Sci & Technol Beijing Donlinks Sch Econ & Management Beijing Peoples R China Minist Educ Beijing Engn Res Ctr MES Technol Iron & Steel Prod Beijing Peoples R China

ISBN: (纸本)9781538636749

关键词： permutation flowshop scheduling problem machine-learning estimation of distribution algorithm total tardiness

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Using Markov Chain Based estimation of distribution algorithm for Model-Based Safety Analysis of Graph Transformation

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Journal of Computer Science & Technology 2021年第4期36卷 839-855页

作者： Einollah Pira Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz ***Iran

The ability to assess the reliability of safety-critical systems is one of the most crucial requirements in the design of modern safety-critical systems where even a minor failure can result in loss of life or irreparable damage to the *** checking is an automatic technique that verifies or refutes system properties by exploring all reachable states(state space)of a *** large and complex systems,it is probable that the state space explosion problem *** exploring the state space of systems modeled by graph transformations,the rule applied on the current state specifies the rule that can perform on the next *** other words,the allowed rule on the current state depends only on the applied rule on the previous state,not the ones on earlier *** fact motivates us to use a Markov chain(MC)to capture this type of dependencies and applies the estimation of distribution algorithm(EDA)to improve the quality of the *** is an evolutionary algorithm directing the search for the optimal solution by learning and sampling probabilistic models through the best individuals of a population at each *** show the effectiveness of the proposed approach,we implement it in GROOVE,an open source toolset for designing and model checking graph transformation *** results confirm that the proposed approach has a high speed and accuracy in comparison with the existing meta-heuristic and evolutionary techniques in safety analysis of systems specified formally through graph transformations.

关键词： safety analysis model checking Markov chain estimation of distribution algorithm graph transformation system

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Histogram-Based estimation of distribution algorithm:A Competent Method for Continuous Optimization

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Journal of Computer Science & Technology 2008年第1期23卷 35-43页

作者：丁楠周树德孙增圻 Department of Electronic Engineering Tsinghua University Department of Computer Science and Technology Tsinghua University

Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. This paper utilizes histogram probabilistic model to describe the distribution of population and to generate promising solutions. The advantage of histogram model, its intrinsic multimodality, makes it proper to describe the solution distribution of complex and multimodal continuous problems. To make histogram model more efficiently explore and exploit the search space, several strategies are brought into the algorithms： the surrounding effect reduces the population size in estimating the model with a certain number of the bins and the shrinking strategy guarantees the accuracy of optimal solutions. Furthermore, this paper shows that histogram-based EDA （estimation of distribution algorithm） can give comparable or even much better performance than those predominant EDAs based on Gaussian models.

关键词： evolutionary algorithm estimation of distribution algorithm histogram probabilistic model surrounding effect shrinking strategy

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An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2007年第12期58卷 1574-1585页

作者： Aickelin, U. Burke, E. K. Li, J. Univ Nottingham Sch Comp Sci & IT Nottingham NG8 1BB England

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work ( where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, that is, we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, that is, an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (ie the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real-world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules.

关键词： nurse rostering estimation of distribution algorithm local search ant colony optimization

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A continuous estimation of distribution algorithm for the online order-batching problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第1-4期79卷 569-588页

作者： Perez-Rodriguez, Ricardo Hernandez-Aguirre, Arturo Joens, S. CIMAT AC Guanajuato 36240 Mexico CONACYT Benito Juarez 03940 DF Mexico

In manual order-picking systems such as picker-toparts, order pickers walk through a warehouse in order to pick up articles required by customers. Order batching consists of combining these customer orders into picking orders. In online batching, customer orders arrive throughout the scheduling. This paper considers an online order-batching problem in which the turnover time of all customer orders has to be minimized, i.e., the time period between the arrival time of the customer order and its completion time. A continuous estimation of distribution algorithm-based approach is proposed and developed to solve the problem and implement the solution. Using this approach, the warehouse performance can be noticeably improved with a substantial reduction in the average turnover time of a set of customer orders.

关键词： estimation of distribution algorithm Warehouse management Order picking Order batching Online optimization

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A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第4期54卷 1039-1060页

作者： Zhao, Fuqing Shao, Zhongshi Wang, Junbiao Zhang, Chuck Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou Peoples R China Northwestern Polytech Univ Key Lab Contemporary Design & Integrated Mfg Tech Minist Educ Xian 710072 Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

Job shop scheduling problem (JSSP) is a typical NP-hard problem. In order to improve the solving efficiency for JSSP, a hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search is proposed in this paper, which combines the merits of estimation of distribution algorithm and Differential evolution (DE). Meanwhile, to strengthen the searching ability of the proposed algorithm, a chaotic strategy is introduced to update the parameters of DE. Two mutation operators are adopted. A neighbourhood search (NS) algorithm based on blocks on critical path is used to further improve the solution quality. Finally, the parametric sensitivity of the proposed algorithm has been analysed based on the Taguchi method of design of experiment. The proposed algorithm was tested through a set of typical benchmark problems of JSSP. The results demonstrated the effectiveness of the proposed algorithm for solving JSSP.

关键词： estimation of distribution algorithm differential evolution algorithm neighbourhood search hybrid optimisation job shop scheduling

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