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检索条件"主题词=Estimation Of Distribution Algorithm"

共 435 条记录，以下是141-150 订阅

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A Multiobjective estimation of distribution algorithm Based on Artificial Bee Colony

A Multiobjective Estimation of Distribution Algorithm Based ...

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1st BRICS Countries Congress on Computational Intelligence / 11th Brazilian Congress on Computational Intelligence (BRICS-CCI and CBIC)

作者： Novais, Fabiano T. Batista, Lucas S. Rocha, Agnaldo J. Guimaraes, Frederico G. Univ Fed Ouro Preto Dept Comp BR-35400000 Ouro Preto MG Brazil Univ Fed Ouro Preto Dept Controle & Automacao BR-35400000 Ouro Preto MG Brazil Univ Fed Minas Gerais Dept Engn Eletr BR-31270901 Belo Horizonte MG Brazil

ISBN: (纸本)9781479931941

In this paper, we propose a hybrid Multiobjective estimation of distribution algorithm based on Artificial Bee Colonies and Clusters (MOEDABC) to solve multiobjective optimization problems with continuous variables. This algorithm is inspired in the organization and division of work in a bee colony and employs techniques from estimation of distribution algorithms. To improve some estimations we also employ clustering methods in the objective space. In the MOEDABC model, the colony consists of four groups of bees, each of which with its specific role in the colony: employer bees, onlookers, farmers and scouts. Each role is associated to specific tasks in the optimization process and employs different estimation of distribution methods. By combining estimation of distribution, clusterization of the objective domain, and the crowding distance assignment of NSGA-II, it was possible to extract more information about the optimization problem, thus enabling an efficient solution of large scale decision variable problems. Regarding the test problems, quality indicators, and GDE3, MOEA/D and NSGA-II methods, the combination of strategies incorporated into the MOEDABC algorithm has presented competitive results, which indicate this method as a useful optimization tool for the class of problems considered.

关键词： Multiobjective Swarm Intelligence estimation of distribution algorithm Clusters

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An estimation of distribution algorithm for Solving Hybrid Flow-shop Scheduling Problem with Stochastic Processing Time

An Estimation of Distribution Algorithm for Solving Hybrid F...

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第三十二届中国控制会议

作者： WANG Shengyao WANG Ling XU Ye Tsinghua National Laboratory for Information Science and Technology(TNList) Department of AutomationTsinghua University

ISBN: (纸本)9781479900305

In this paper,an effective estimation of distribution algorithm(EDA) is proposed to solve the hybrid flow-shop scheduling problem with stochastic processing *** the effectiveness and robustness of a schedule,the schedule objective is to minimize the makespan of the initial scenario as well as the deviation of the makespan between all stochastic scenarios and the initial *** the proposed EDA,a bi-objective evaluation function is employed to evaluate the individuals of the population.A probability model is presented to describe the probability distribution of the solution space.A mechanism is provided to update the probability model with the superior *** sampling the probability model,new individuals can be generated among the search region with the promising *** testing results based on some well known benchmark instances are *** comparisons with the existing genetic algorithm demonstrate the effectiveness and robustness of the proposed EDA.

关键词： Hybrid flow-shop scheduling problem stochastic processing time estimation of distribution algorithm probability model robust scheduling

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Hybrid estimation of distribution algorithm for permutation flowshop scheduling problem with sequence dependent family setup times

Hybrid Estimation of Distribution Algorithm for permutation ...

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International Conference on Computers and Industrial Engineering (CIE39)

作者： Eddaly, Mansour Jarboui, Bassem Bouabda, Radhouan Rebai, Abdelwaheb Univ Sfax FSEGS Route Aroport Km 4 Sfax 3018 Tunisia Univ Sfax ESC Sfax 3018 Tunisia

ISBN: (纸本)9781424441358

This paper addresses to the scheduling in manufacturing cell environment with sequence dependent family setup times in a flow shop with respect to the makespan criterion. Since this is a NP-hard problem, we present an estimation of distribution algorithm as an evolutionary algorithm for solving it. In order to improve the quality of solution of our algorithm, we propose a hybridization with an iterated local search algorithm. The computational experiments show that our algorithm is better than the evolutionary algorithms proposed in the literature. Moreover, it seems able to provide good results in short computational time.

关键词： manufacturing cell flowshop sequence dependant family estimation of distribution algorithm iterated local search

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An estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem

An estimation of distribution algorithm for the multi-object...

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4th IEEE Symposium on Computational Intelligence in Scheduling (CISched)

作者： Wang, Shengyao Wang, Ling Liu, Min Xu, Ye Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China

ISBN: (纸本)9781467359092

In this paper, an effective estimation of distribution algorithm (EDA) is proposed to solve the multi-objective flexible job-shop scheduling problem (MFJSP) to minimize the maximum completion time, the total workload of machines and the workload of the critical machine simultaneously. Within the framework of EDA, the new individuals are generated by sampling a probability model, which is built and updated with the superior sub-population by a proposed mechanism. Moreover, the EDA utilizes multiple strategies in a combination way to generate the initial solutions, and used a local search strategy based on critical path to enhance the exploitation ability. Based on the Taguchi method of design-of-experiment, the influence of parameter setting is investigated and suitable parameters are suggested. Finally, numerical simulation based on some well-known benchmarks and comparisons with some existing algorithms are carried out. The results demonstrate the effectiveness of the proposed EDA to solve the MFJSP.

关键词： estimation of distribution algorithm multi-objective flexible job-shop scheduling problem probability model critical path design of experiment

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Dynamic Optimization of Chemical Engineering Problems Using Affinity Propagation Based estimation of distribution algorithm 11

Dynamic Optimization of Chemical Engineering Problems Using ...

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11th World Congress on Intelligent Control and Automation

作者： Luo, Na Feng, Wei Wang, Xiaoqiang Qian, Feng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai Peoples R China

ISBN: (纸本)9781479958252

Dynamic optimization has attracted much attention for its wide applications in engineering problems. However, it is still a challenge for high nonlinear, multi-dimensional and multimodal problems. estimation of distribution algorithm was proposed in which probabilistic models extracted relevant features of the complex search space and then generated new individuals during optimization. In order to decrease the dependences among control variables in dynamic optimization, affinity propagation was applied to cluster the individuals in evolutionary iterations. In each cluster, the probabilistic density function of Gaussian mixture model refined the promising spaces with high quality solutions and avoided the random combination of different control variables. To evaluate the performance of the new approach, three dynamic optimization problems of chemical process are used as cases comparing with three state-of-the-art global optimization methods. The results showed that the new approach could achieve the best solution in most cases with less computational effort and higher efficiency.

关键词： Dynamic optimization estimation of distribution algorithm Chemical process affinity propagation Gaussian mixture model

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Enhance Continuous estimation of distribution algorithm by Variance Enlargement and Reflecting Sampling

Enhance Continuous Estimation of Distribution Algorithm by V...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Ren, Zhigang He, Chenlong Zhong, Dexing Huang, Shanshan Liang, Yongsheng Xi An Jiao Tong Univ Sch Elect & Informat Engn Dept Automat Sci & Technol Xian Peoples R China

ISBN: (纸本)9781509006229

estimation of distribution algorithm (EDA) is a kind of typical model-based evolutionary algorithm (EA). Although possessing competitive advantages in theoretical analysis, current EDAs may encounter premature convergence due to the rapid shrinkage of the search range and the relatively low sampling efficiency. Focusing on continuous EDAs with Gaussian models, this paper proposes a novel probability density estimator which can adaptively enlarge the variances and thus endow EDA with flexible search behavior. For the estimated probability density, a reflecting sampling strategy which can further improve the search efficiency is put forward. With these two algorithmic strategies, a new EDA variant named EDA(ve-rs) is developed. Experimental results on a set of benchmark problems demonstrate that EDA(ve-rs) outperforms conventional EDAs and can produce superior solutions in comparison with some state-oft-he-art EAs.

关键词： estimation of distribution algorithm premature convergence probability density reflecting sampling

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Application of estimation of distribution algorithm for Feature Selection 19

Application of Estimation of Distribution Algorithm for Feat...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Ayodele, Mayowa Univ Manchester Manchester Lancs England

ISBN: (纸本)9781450367486

Feature selection is a machine learning concept that entails selecting relevant features while eliminating irrelevant and redundant features. This process helps to speed up learning. In this paper, an estimation of distribution algorithm (EDA) is applied to a feature selection problem originating from a legal business. The EDA was able to generate a realistic solution to the real-world problem.

关键词： Feature Selection Support Vector Machine estimation of distribution algorithm

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An improved estimation of distribution algorithm for Solving Constrained Mixed-Integer Nonlinear Programming Problems

An improved Estimation of Distribution Algorithm for Solving...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Molina Perez, Daniel Alfredo Portilla-Flores, Edgar Mezura-Montes, Efren Vega-Alvarado, Eduardo Inst Politecn Nacl CIDETEC Ciudad De Mexico Mexico Inst Politecn Nacl UPIIT Tlaxcala Mexico Univ Veracruz Artificial Intelligence Res Ctr Veracruz Veracruz Mexico

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

In a mixed-integer nonlinear programming problem, integer restrictions divide the feasible region into discontinuous feasible parts with different sizes. Evolutionary algorithms (EAs) are usually vulnerable to being trapped in larger discontinuous feasible parts. In this work, an improved version of an estimation of distribution algorithm (EDA) is developed, where two new operations are proposed. The first one establishes a link between the learning-based histogram model and the s-constrained method. Here, the constraint violation level of the s-constrained method is used to explore the smaller discontinuous parts and form a better statistical model. The second operation is the hybridization of the EDA with a mutation operator to generate offspring from both the global distribution information and the parent information. A benchmark is used to test the performance of the improved proposal. The results indicated that the proposed approach shows a better performance against other tested EAs. This new proposal solves to a great extent the influence of the larger discontinuous feasible parts, and improve the local refinement of the real variables.

关键词： estimation of distribution algorithm evolutionary algorithms integer restriction handling mixed integer nonlinear programming

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A Surrogate Model Assisted estimation of distribution algorithm with Mutil-acquisition Functions for Expensive Optimization

A Surrogate Model Assisted Estimation of Distribution Algori...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Hao, Hao Wang, Shuai Li, Bingdong Zhou, Aimin East China Normal Univ Shanghai Frontiers Sci Ctr Mol Intelligent Synth Sch Comp Sci & Technol Shanghai Inst AI Educ Shanghai Peoples R China

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

The estimation of distribution algorithm (EDA) is an efficient heuristic method for handling black-box optimization problems since the ability for global population distribution modeling and gradient-free searching. However, the trial and error search mechanism relies on a large number of function evaluations, which is a considerable challenge under expensive black-box problems. Therefore, this article presents a surrogate assisted EDA with multi-acquisition functions. Firstly, a variable-width histogram is used as the global distribution model that focuses on promising areas. Next, the evaluated-free local search method improves the quality of new generation solutions. Finally, model management with multiple acquisitions maintains global and local exploration preferences. Several commonly used benchmark functions with 20 and 50 dimensions are adopted to evaluate the proposed algorithm compared with several state-of-the-art surrogate assisted evaluation algorithms (SAEAs) and Bayesian optimization method. In addition, a rover trajectories optimizing problem is used to verify the ability to solve complex problems. The experimental results demonstrate the superiority of the proposed algorithm over these comparison algorithms.

关键词： Surrogate assisted Evolutionary algorithm estimation of distribution algorithm Acquisition functions

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An Efficient estimation of distribution algorithm for Job Shop Scheduling Problem

An Efficient Estimation of Distribution Algorithm for Job Sh...

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1st International Conference on Swarm, Evolutionary, and Memetic Computing

作者： He, Xiao-juan Zeng, Jian-chao Xue, Song-dong Wang, Li-fang Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Peoples R China

ISBN: (纸本)9783642175626

An estimation of distribution algorithm with probability model based on permutation information of neighboring operations for job shop scheduling problem was proposed. The probability model was given using frequency information of pair-wise operations neighboring. Then the structure of optimal individual was marked and the operations of optimal individual were partitioned to some independent sub-blocks. To avoid repeating search in same area and improve search speed, each sub-block was taken as a whole to be adjusted. Also, stochastic adjustment to the operations within each sub-block was introduced to enhance the local search ability. The experimental results show that the proposed algorithm is more robust and efficient.

关键词： Job Shop scheduling problem estimation of distribution algorithm neighboring operations probability model

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