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作者： Slezak, J. Brno University of Technology

The paper is focused on evolutionary synthesis of analog circuit realization of cube root function using proposed Graph Hybrid estimation of distribution algorithm. The problem of cube root function circuit realization was adopted to demonstrate synthesis capability of the proposed method. Individuals of the population of the proposed method which represent promising topologies are encoded using graphs and hypergraphs. Hybridization with local search algorithm was used. The proposed method employs univariate probabilistic model.

关键词： Automated analog circuit synthesis evolutionary algorithm analog circuit design estimation of distribution algorithm computational circuit univariate marginal distribution algorithm

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Evaluation of estimation of distribution algorithm to Calibrate Computationally Intensive Hydrologic Model

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JOURNAL OF HYDROLOGIC ENGINEERING 2016年第6期21卷

作者： Li, Zejun Liu, Pan Deng, Chao Guo, Shenglian He, Ping Wang, Caijun Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China Wuhan Univ Coll Water Resources & Hydropower Hubei Prov Collaborat Innovat Ctr Water Resources 8 Donghu Rd South Wuhan 430072 Peoples R China China Int Engn Consulting Corp Zhongzi Bldg32Chegongzhuang Rd West Beijing 100048 Peoples R China

The estimation of distribution algorithm(EDA) is a new evolutionary algorithm developed as an alternative to the traditional genetic algorithm (GA). The EDA guides the search by avoiding the crossover and mutation operators of the GA in favor of building and sampling probabilistic distributions of promising candidate solutions. By increasing the probability of generating solutions with better fitness values, the EDA locates the region of the global optimum or its accurate approximation. In this study, EDA was used to calibrate the parameters of the soil and water assessment tool hydrologic model for the Xunhe River Basin in China. The EDA was compared with three other algorithms: (1) the Multistart Local Metric Stochastic Radial Basis Function algorithm (a surrogate optimization method), (2) the Shuffled Complex Evolution algorithm, and (3) the GA. Four metrics are presented to assess the performance of the algorithms: (1) efficiency in terms of the average best objective function value in a limited number of function evaluations, (2) variability in terms of standard deviation and the box plot, (3) reliability in terms of the empirical cumulative distribution function, and (4) accuracy in terms of the Nash-Sutcliffe efficiency coefficient and overall volume error. Results indicated that the EDA is more efficient and could provide more accurate solutions with a relatively high probability, at least for this case study. (C) 2016 American Society of Civil Engineers.

关键词： estimation of distribution algorithm Hydrologic model Parameter calibration

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Language model based interactive estimation of distribution algorithm

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KNOWLEDGE-BASED SYSTEMS 2020年 200卷 105980-105980页

作者： Chen, Yang Jin, Yaochu Sun, Xiaoyan China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Univ Surrey Dept Comp Sci Guildford GU4 7YX Surrey England Nanyang Technol Univ Sch Comp Sci & Engn 50 Nanyang Ave Singapore 639798 Singapore

It is very hard, if not impossible to use analytical objective functions for optimization of personalized search due to the difficulties in mathematically describing qualitative problems. To solve such optimization problems, interactive evolutionary algorithms, which can make use of human preferences, are highly desirable. However, due to the lack of effective encoding methods, interactive evolutionary algorithms have been limited to numerically encoded optimization problems. In practice, however, linguistic terms (words) are the most natural expression of human preferences, and they are also commonly used to describe items in personalized search or E-commerce;therefore, language models better suit encoding, and the optimization of personalized search is converted into a dynamic document matching problem. To optimize word-described personalized search, we propose a novel interactive estimation of distribution algorithm. This algorithm combines a language model-based encoding approach, a Dirichlet-Multinomial compound distribution-based preference expression, and a Bayesian inference mechanism. The proposed algorithm is applied to two personalized search cases to demonstrate the capability of the algorithm in ensuring a more efficient and accurate search with less user fatigue. (C) 2020 Elsevier B.V. All rights reserved.

关键词： estimation of distribution algorithm Interactive evolutionary algorithm Language model Personalized search Bayesian inference

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Scheduling of energy-efficient distributed blocking flowshop using pareto-based estimation of distribution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 200卷 116910-116910页

作者： Zhang, Xiaohui Liu, Xinhua Cichon, Andrzej Krolczyk, Grzegorz Li, Zhixiong Xuzhou Univ Technol Sch Elect & Control Engn Xuzhou 221018 Jiangsu Peoples R China China Univ Min & Technol Sch Mech & Elect Engn Xuzhou 211006 Jiangsu Peoples R China Technol & Innovat Res Ctr JiangYan EDZ Taizhou 225500 Peoples R China Opole Univ Technol Dept Elect Control & Comp Engn PL-45758 Opole Poland Opole Univ Technol Dept Mfg Engn & Automat Prod PL-45758 Opole Poland Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

This study investigates the impact of production scheduling decisions aims at improving productive and energy- efficient performances simultaneously in distributed blocking flowshops (EDBFSP). To reach a compromise be-tween the conflicting objectives, a Pareto multi-objective optimization model based on the estimation of dis-tribution algorithm (MOEDA) is proposed. Firstly, an initialization method based on the problem-specific characteristics is designed to create a promising population with quality and diversity;secondly, a probabilistic model based on a Bayesian network is constructed to predict position relationships between jobs. Two neigh-borhood operators with modified insertion technique are proposed to realize the adjustments of both job sequence and processing speed;thirdly, two operators are developed to execute multi-objective local searches on the elite solutions. Aiming at efficient utilization of the resulted blocking and idle time, an energy-saving method is designed for EDBFSP. In the experimental parts, to gain the best performance, the key parameters of MOEDA have been calibrated. The validation is conducted to assess the performances of the designed initialization method, neighborhood search, local search, and energy-saving strategies. The proposed MOEDA is also compared with mainstream metaheuristics for solving green scheduling problems. The experiment results show that the optimization and search ability of MOEDA have gained prominent advantages over other metaheuristics in both precision and distributivity.

关键词： estimation of distribution algorithm Blocking constraint Multi-objective optimization Energy efficiency

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

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 60卷

作者： Zhang, Zi-Qi Qian, Bin Hu, Rong Jin, Huai-Ping Wang, Ling 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

The distributed assembly permutation flow-shop scheduling problem (DAPFSP) is a typical NP-hard combinatorial optimization problem that has wide applications in advanced manufacturing systems and modern supply chains. In this work, an innovative three-dimensional matrix-cube-based estimation of distribution algorithm (MCEDA) is first proposed for the DAPFSP to minimize the maximum completion time. Firstly, a matrix cube is designed to learn the valuable information from elites. Secondly, a matrix-cube-based probabilistic model with an effective sampling mechanism is developed to estimate the probability distribution of superior solutions and to perform the global exploration for finding promising regions. Thirdly, a problem-dependent variable neighborhood descent method is proposed to perform the local exploitation around these promising regions, and several speedup strategies for evaluating neighboring solutions are utilized to enhance the computational efficiency. Furthermore, the influence of the parameters setting is analyzed by using design-of-experiment technique, and the suitable parameters are suggested for different scale problems. Finally, a comprehensive computational campaign against the state-of-the-art algorithms in the literature, together with statistical analyses, demonstrates that the proposed MCEDA produces better results than the existing algorithms by a significant margin. Moreover, the new best-known solutions for 214 instances are improved.

关键词： Scheduling Matrix cube estimation of distribution algorithm Distributed assembly permutation flow shop Variable neighborhood descent

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A coevolutionary estimation of distribution algorithm based on dynamic differential grouping for mixed-variable optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Huang, Shijia Wang, Zhe Ge, Yang Wang, Feng Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

The mixed variable optimization problems (MVOPs), which involves both continuous and discrete decision variables, are difficult to be solved due to the complex search space. Recently, many EA-based algorithms have been designed to address MVOPs. However, due to the mixed variables with different evolutionary operators and complex search space, it is difficult to handle the mixed variables effectively and the search efficiency cannot be guaranteed. How to solve MVOPs efficiently has been a challengeable issue. In this paper, we propose a mixed-variable optimization algorithm called coevolutionary estimation of distribution algorithm (CoEDAmv). First, a dynamic differential grouping (DDG) method is employed to improve the search efficiency of CoEDAmv, in which both the interaction of variables and search performance on the current search region are considered simultaneously. Second, two probabilistic models, i.e. fitness rank based continuous histogram (FRCH) and fitness rank based discrete histogram (FRDH), are proposed to handle continuous and discrete variables respectively, which can benefit from elite individuals obtained during the fitness ranking strategy and enhance the convergence performance with the elite neighborhood-based updating probability strategy. Compared with eight state -of -the -art algorithms, the experimental results on 28 artificial MVOPs show that CoEDAmv is effective and efficient.

关键词： Mixed-variable optimization Dynamic differential grouping estimation of distribution algorithm Coevolutionary algorithms

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Novel operating theatre scheduling method based on estimation of distribution algorithm

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Journal of Southeast University(English Edition) 2016年第1期32卷 112-118页

作者：周炳海殷萌同济大学机械与能源工程学院上海201804

In order to improve the efficiency of operating rooms,reduce the costs for hospitals and improve the level of service qualities, a scheduling method was developed based on an estimation of distribution algorithm（ EDA）. First, a scheduling problem domain is described. Based on assignment constraints and resource capacity constraints, the mathematical programming models are set up with an objective function to minimize the system makespan. On the basis of the descriptions mentioned above, a solution policy of generating feasible scheduling solutions is established. Combined with the specific constraints of operating theatres, the EDA-based algorithm is put forward to solve scheduling problems. Finally, simulation experiments are designed to evaluate the scheduling method. The orthogonal table is chosen to determine the parameters in the proposed method. Then the genetic algorithm and the particle swarm optimization algorithm are chosen for comparison with the EDA-based algorithm, and the results indicate that the proposed method can decrease the makespan of the surgical system regardless of the size of operations. Moreover, the computation time of the EDA-based algorithm is only approximately 5 s when solving the large scale problems, which means that the proposed algorithm is suitable for carrying out an on-line scheduling optimization of the patients.

关键词： operating theatre scheduling estimation of distribution algorithm makespan

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A hybrid estimation of distribution algorithm for solving assembly flexible job shop scheduling in a distributed environment

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartE期133卷

作者： Du, Baigang Han, Shuai Guo, Jun Li, Yibing Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China

This paper proposes a novel distributed assembly flexible job shop scheduling problem (DAFJSP), which involves three stages: production stage, assembly stage, and delivery stage. The production stage is accomplished in a few flexible job shops, the assembly stage is accomplished in a few single -machine factories, and the delivery stage is to deliver the obtained products to the corresponding customers. To address the problem, a hybrid estimation of distribution algorithm based on differential evolution operator and variable neighborhood search (HEDA-DEV) is proposed with the goal of minimizing the total cost and tardiness. Firstly, a new multidimensional coding method is designed based on the features of the DAFJSP. Secondly, two mutation operators and the similarity coefficient based on the probability matrix are put forward to implement the dynamic mutation. Thirdly, five types of neighborhood structures satisfying cooperative search strategies are employed to adequately improve the local exploitation ability. Finally, the comparison experiment results suggest that the proposed HEDA-DEV has competitive performance compared to the selected efficient algorithms. Moreover, a real case study is used to demonstrate that HEDA-DEV is an effective method for solving DAFJSP.

关键词： Distributed assembly flexible job shop scheduling problem estimation of distribution algorithm Differential evolution Variable neighborhood search

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An enhanced estimation of distribution algorithm with problem-specific knowledge for distributed no-wait flowshop group scheduling problems

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 87卷

作者： Zhang, Zi-Qi Xu, Yan-Xuan Qian, Bin Hu, Rong Wu, Fang-Chun Wang, Ling Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 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

With the trend of economic globalization, distributed manufacturing widely exists in modern manufacturing systems. As an extension of the distributed flowshop scheduling problem, the distributed no -wait flowshop group scheduling problem with sequence -dependent setup times (DNFGSP_SDSTs) is investigated in this article. To address DNFGSP_SDSTs with the criterion of minimizing makespan, this study proposes an enhanced estimation of distribution algorithm & sdot;(EEDA) with problem -specific knowledge. First, a mixed integer linear programming (MILP) model of DNFGSP_SDSTs is established. Second, based on the characteristics of DNFGSP_SDSTs, five problem -specific properties about local search operators are derived as prior knowledge to reduce computational cost. Third, two NEH-based two -stage heuristics are presented to construct a high -quality population with diversity. Fourth, a probability model with problem -specific knowledge and a family -based updating mechanism are developed to accumulate valuable pattern information from high -quality solutions, while a sampling strategy is designed to generate new populations with the accumulated information. Fifth, several local search operators are devised to refine the obtained solutions. Furthermore, perturbation and reinitialization methods are developed to avoid premature convergence. Finally, the validity of the MILP model is verified by using the Gurobi solver. The parameters of EEDA are tuned through a design of experiments. The effectiveness of key components in EEDA is confirmed through extensive experiments, and the computational comparisons with the state-of-theart algorithms indicate the effectiveness of the proposed EEDA for solving DNFGSP_SDSTs.

关键词： Flowshop group scheduling Distributed no-wait flowshop group scheduling estimation of distribution algorithm Problem-specific knowledge

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CONTROLLING CHAOS BY AN IMPROVED estimation of distribution algorithm

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MATHEMATICAL & COMPUTATIONAL APPLICATIONS 2010年第5期15卷 866-871页

作者： Huang, Xingli Jia, Peifa Liu, Bo Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Ctr Forecasting Sci Beijing 100190 Peoples R China

Control and synchronization of chaotic systems are important issues in nonlinear sciences. This paper proposes an effective estimation of distribution algorithm (EDA)-based memetic algorithm (MA) to direct the orbits of discrete chaotic dynamical systems as well as to synchronize chaotic systems, which could be formulated as complex multi-modal numerical optimization problems. In EDA-based MA (EDAMA), both EDA-based searching operators and simulated annealing (SA) based local searching operators are designed to balance the exploration and exploitation abilities. On the other hand, global information provided by EDA is combined with local information from SA to create better solutions. In particular, to enrich the searching behaviors and to avoid premature convergence, SA-based local search is designed and incorporated into EDAMA. To balance the exploration and exploitation abilities, after the standard EDA-based searching operation, SA-based local search is probabilistically applied to some good solutions selected by using a roulette wheel mechanism with a specified probability. Numerical simulations based on Henon Map demonstrate the effectiveness and efficiency of EDAMA, and the effects of some parameters are investigated as well.

关键词： estimation of distribution algorithm Chaotic dynamics

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