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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Ho, Tsung-Yu Yu, Tian-Li Natl Taiwan Univ Dept Elect Engn Taiwan Evolutionary Intelligence Lab 1Sect 4Roosevelt Rd Taipei Taiwan

ISBN: (纸本)9781450311786

This work proposes a linkage-learning nicking method that improves the capability of estimation of distribution algorithms (EDAs) on reducing spurious linkages which increase problems difficulty. Concatenated parity function (CPF), a class of allelic pairwise independent problems, causes exponential scalability for hierarchical Bayesian optimization algorithm (hBOA), which is one of powerful EDAs. Empirical results show that restricted tournament replacement (RTR) that hBOA employs results in spurious linkages and increases difficulty on solving CPF. Our research consists of these goals: (1) proposing a mutual information matrix to approximate the implicit linkage-information during EDAs' execution, (2) reducing spurious linkages by utilizing new metric of similarity, and (3) maintaining diversity of population. The results show that hBOA with our proposed nicking method reduces the spurious linkages and solves CPF in the polynomial time.

关键词： Genetic algorithm estimation of distribution algorithm Pairwise Linkage Independent Linkage Learning Niching

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A t-level driven search for estimation of distribution algorithm in solving task graph allocation to multiprocessors 13

A t-level driven search for estimation of distribution algor...

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13th IEEE Conference on Automation Science and Engineering (IEEE CASE)

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

ISBN: (纸本)9781509067817

The development of cloud computing drives the research on parallel processing. One of the important problems in parallel processing is to minimize the makespan of the tasks with precedence constraints on multiprocessors scheduling. In this paper, the property of the t-level (top-level) is analyzed, and a t-level (top level) driven search is proposed to enhance the exploitation ability of the efficient estimation of distributed algorithm (eEDA), which was developed for solving the precedence constrained scheduling problem. Numerical tests and comparisons are carried out. The results demonstrate that the t-level driven search is able to improve the optimization capacity of the eEDA under heterogeneous multiprocessor situation. Moreover, it is also shown that the eEDA with the t-level driven search on homogeneous computing systems is effective.

关键词： t-level task graph allocation estimation of distribution algorithm parallel computing system

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An Effective estimation of distribution algorithm for Multi-track Train Scheduling Problem

An Effective Estimation of Distribution Algorithm for Multi-...

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10th International Conference on Intelligent Computing (ICIC)

作者： Wang, Shengyao Wang, Ling Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

ISBN: (纸本)9783319093390;9783319093383

In this paper, an effective estimation of distribution algorithm (EDA) is presented for solving the multi-track train scheduling problem (MTTSP). The individual of the EDA is represented as the permutation of train priority. With a proper track assignment rule, the individual is decoded into feasible schedule. In addition, the EDA builds a probability model for describing the distribution of the solution space. In every generation, it samples the promising region for generating new individuals and updates the probability model with the superior population. Moreover, the influence of parameter setting is investigated based on design-of-experiment method and a set of suitable parameter values is suggested. Simulation results based on some instances and comparisons with the existing algorithm demonstrate the effectiveness and efficiency of the EDA.

关键词： train scheduling assignment rule estimation of distribution algorithm probability model

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An estimation of distribution algorithm for the Flexible Job-Shop Scheduling Problem

An Estimation of Distribution Algorithm for the Flexible Job...

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7th International Conference on Intelligent Computing (ICIC)

作者： Wang, Shengyao Wang, Ling Zhou, Gang Xu, Ye Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

ISBN: (纸本)9783642259432

In this paper, an effective estimation of distribution algorithm (EDA) is proposed to solve the flexible job-shop scheduling problem with the criterion to minimize the maximum completion time (makespan). With the framework of the EDA, the probability model is built with the superior population and the new individuals are generated based on probability model. In addition, an updating mechanism of the probability model is proposed and a local search strategy based on critical path is designed to enhance the exploitation ability. Finally, numerical simulation is carried out based on the benchmark instances, and the comparisons with some existing algorithms demonstrate the effectiveness of the proposed algorithm.

关键词： flexible job-shop scheduling problem makespan estimation of distribution algorithm probability model critical path

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A HYBRID MULTI-OBJECTIVE BAYESIAN estimation of distribution algorithm

A HYBRID MULTI-OBJECTIVE BAYESIAN ESTIMATION OF DISTRIBUTION...

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作者： MARCELLA SCOCZYNSKI RIBEIRO MARTINS FEDERAL UNIVERSITY OF TECHNOLOGY - PARANA

学位级别：博士

Nowadays, a number of metaheuristics have been developed for dealing with multiobjective optimization problems. estimation of distribution algorithms (EDAs) are a special class of metaheuristics that explore the decision variable space to construct probabilistic models from promising solutions. The probabilistic model used in EDA captures statistics of decision variables and their interdependencies with the optimization problem. Moreover, the aggregation of local search methods can notably improve the results of multi-objective evolutionary algorithms. Therefore, these hybrid approaches have been jointly applied to multi-objective problems. In this work, a Hybrid Multi-objective Bayesian estimation of distribution algorithm (HMOBEDA), which is based on a Bayesian network, is proposed to multi and many objective scenarios by modeling the joint probability of decision variables, objectives, and configuration parameters of an embedded local search (LS). We tested different versions of HMOBEDA using instances of the multi-objective knapsack problem for two to five and eight objectives. HMOBEDA is also compared with five cutting edge evolutionary algorithms (including a modified version of NSGA-III, for combinatorial optimization) applied to the same knapsack instances, as well to a set of MNK-landscape instances for two, three, five and eight objectives. An analysis of the resulting Bayesian network structures and parameters has also been carried to evaluate the approximated Pareto front from a probabilistic point of view, and also to evaluate how the interactions among variables, objectives and local search parameters are captured by the Bayesian networks. Results show that HMOBEDA outperforms the other approaches. It not only provides the best values for hypervolume, capacity and inverted generational distance indicators in most of the experiments, but it also presents a high diversity solution set close to the estimated Pareto front.

关键词： Multi-objective optimization metaheuristic bayesian network estimation of distribution algorithm local search hybridization

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Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm

Multivariate Markov networks for fitness modelling in an est...

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作者： Brownlee, Alexander Edward Ian Robert Gordon University

学位级别：master

A well-known paradigm for optimisation is the evolutionary algorithm (EA). An EA maintainsa population of possible solutions to a problem which converges on a global optimumusing biologically-inspired selection and reproduction operators. These algorithms havebeen shown to perform well on a variety of hard optimisation and search problems.A recent development in evolutionary computation is the estimation of distributionalgorithm (EDA) which replaces the traditional genetic reproduction operators (crossoverand mutation) with the construction and sampling of a probabilistic model. While this canoften represent a significant computational expense, the benefit is that the model containsexplicit information about the fitness *** thesis expands on recent work using a Markov network to model fitness in anEDA, resulting in what we call the Markov Fitness Model (MFM). The work has exploredthe theoretical foundations of the MFM approach which are grounded in Walsh analysisof fitness functions. This has allowed us to demonstrate a clear relationship between thefitness model and the underlying dynamics of the problem. A key achievement is that wehave been able to show how the model can be used to predict fitness and have deviseda measure of fitness modelling capability called the fitness prediction correlation (FPC).We have performed a series of experiments which use the FPC to investigate the effect ofpopulation size and selection operator on the fitness modelling capability. The results andanalysis of these experiments are an important addition to other work on diversity andfitness distribution within *** this improved understanding of fitness modelling we have been able to extend theframework distribution estimation Using Markov networks (DEUM) to use a multivariateprobabilistic model. We have proposed and demonstrated the performance of a numberof algorithms based on this framework which lever the MFM for optimisation, which cannow be added to the EA

关键词： Evolutionary algorithm EDA estimation of distribution algorithm Markov networks

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Scheduling optimization of underground mine trackless transportation based on improved estimation of distribution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Li, Ning Wu, Yahui Ye, Haiwang Wang, Liguan Wang, Qizhou Jia, Mingtao Wuhan Univ Technol Hubei Key Lab Mineral Resources Proc & Environm Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Sch Resource & Environm Engn Wuhan 430070 Hubei Peoples R China Cent South Univ Sch Resource & Safety Engn Changsha 410083 Hunan Peoples R China

The trend in underground mine development is trackless transportation, and the scheduling optimization of underground mine trackless transportation is a current research hotspot. This paper proposes a truck scheduling optimization method for underground mine trackless transportation based on an improved estimation of distribution algorithm to address the truck scheduling problem in the underground mine trackless transportation process. The transportation process of transport trucks in underground mines is analyzed. The dispatching model of transport trucks in underground mines is constructed based on the requirements of reducing transportation costs and increasing transportation efficiencies, taking into account the truck meeting situation in the ramp section and minimizing the total shift transportation distance and the total waiting time of transport trucks as the objective functions. The improved estimation of distribution algorithm is used to solve the truck scheduling model, resulting in the optimal ore blending and scheduling schemes. The comparative analysis employs a genetic algorithm, particle swarm optimization algorithm, and immune algorithm. The results demonstrate that, compared to other algorithms, the improved estimation of distribution algorithm proposed in this paper has superior performance in terms of convergence speed and the search for the optimal solution. The total number of transportation tasks associated with the optimal ore allocation scheme is at least 82, and the waiting time associated with the optimal scheduling scheme is reduced to 7.5 min. The operation time chart of transport trucks calculated by the optimal dispatching scheme can clearly depict the location of each transport truck at any time during a shift's working time, which has significant guiding significance for the actual truck transportation in the mine.

关键词： Underground mine Trackless transportation estimation of distribution algorithm Ore blending scheme Scheduling optimization

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A matrix-cube-based estimation of distribution algorithm for blocking flow-shop scheduling problem with sequence-dependent setup times

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 205卷

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

The blocking flow-shop scheduling problem with sequence-dependent setup times (BFSP_SDST) is a strong NP- hard problem that exists widely in practice. However, research on this issue is still quite limited. Hence, this paper presents a novel matrix-cube-based estimation of distribution algorithm (MCEDA) to minimize the makespan criterion of the BFSP_SDST. In MCEDA's global search, a matrix cube is devised to reasonably learn the promising patterns in excellent solutions or individuals, and then a matrix-cube-based probabilistic model is developed to quickly guide global search toward the potential promising regions in solution space. A diversity controlling mechanism is also added to avoid the stagnation of global search. In MCEDA's local search, an iterated multi-neighborhood local search controlled by the probabilistic model in global search is designed to execute deeper exploitation from those promising regions. Additionally, two constructive heuristics for gener-ating high-quality initial individuals and one fast Insert-based neighbor evaluation method for accelerating the efficiency of local search are presented based on an analysis of the problem's features. MCEDA's efficacy and superiority in solving the BFSP SDST are demonstrated through comprehensive comparisons with 22 state-of-the- art algorithms.

关键词： Blocking flow-shop scheduling estimation of distribution algorithm Setup times Multi-dimensional probabilistic model Iterated local search

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Cellular estimation of distribution algorithm designed to solve the energy resource management problem under uncertainty

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 101卷 104231-104231页

作者： Martinez-Lopez, Yoan Rodriguez-Gonzalez, Ansel Y. Madera, Julio Bethencourt Mayedo, Miguel Lezama, Fernando Univ Camaguey Camaguey Cuba Univ Cent Las Villas Santa Clara Cuba Consejo Nacl Ciencia & Technol Mexico City DF Mexico Ctr Invest Cient & Educ Super Ensenada CICESE UT3 Unidad Transferencia Tecnol Tepic Mexico Polytech Porto Sch Engn GECAD Porto Portugal

The Energy Resource Management (ERM) can be modeled as a Mixed-Integer Non-Linear Problem whose aim is to maximize profits generally using smart grid capabilities more than importing energy from external markets. Due to this, many resources and customers are involved in optimization, making ERM a complex problem. Moreover, when the inherent uncertainty of weather conditions, load forecast, electric vehicles planned trips, or market prices is considered, deterministic approaches might fail in obtaining optimal solutions to the problem. In this context, evolutionary algorithms are a useful tool to find effective near-optimal solutions. In fact, to design and test evolutionary algorithms to solve the ERM problem under uncertainty, the research community has developed a simulation framework. In this paper, we propose the Cellular Univariate Marginal distribution algorithm with Normal-Cauchy distribution (CUMDANCauchy) to address the ERM problem in uncertain environments. CUMDANCauchy uses a univariate estimation of the product of Normal and Cauchy distributions over each feature, and produces new individuals not only by the sampling of the learned distributions but also using neighborhoods of individuals from a ring cellular structure. The experiments performed over two case studies show that: CUMDANCauchy is as competitive as the previous dominant class of algorithms in terms of the global fitness achieved;its convergence behavior is among the best in comparison with the other tested algorithms;its running time is similar to the algorithm with the best global fitness achieved in the first case study, and it is the fastest algorithm in the second one.

关键词： Energy resource management Evolutionary algorithm estimation of distribution algorithm Smart grid Uncertainty

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Applied Research on Distributed Generation Optimal Allocation Based on Improved estimation of distribution algorithm

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ENERGIES 2018年第9期11卷 2363页

作者： Yang, Lei Yang, Xiaohui Wu, Yue Liu, Xiaoping Nanchang Univ Coll Informat Engn Nanchang 330031 Jiangxi Peoples R China State Grid Jilin Elect Power Co Changchun 130000 Jilin Peoples R China

Most of the current algorithms used to solve the optimal configuration problem in the distributed generation (DG) of electricity depend heavily on control parameters, which may lead to local optimal solutions. To achieve a rapid and effective algorithm of optimized configuration for distributed generation, a hybrid approach combined with Bayesian statistical-inference and distribution estimation is proposed. Specifically, a probability distribution estimation model based on the theory of Bayesian inference is established, then a posteriori probability model with the prior distribution and the conditional distribution is generated, and new individual generators are formed into a dominant group. The information of each individual of this dominant group is used to update the probability model and the updated posteriori probability is used for sampling until the optimal solution is obtained. Finally, the 12 bus, 34 bus and 69 bus radial distribution system is used as an example and comparison is performed to show the effectiveness of the proposed algorithm.

关键词： distributed generation radial distribution system estimation of distribution algorithm bayesian statistical-inference posterior probability

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