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检索条件"主题词=estimation of distribution algorithm"

共 435 条记录，以下是131-140 订阅

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An estimation of distribution algorithm for the channel assignment problem

An estimation of distribution algorithm for the channel assi...

引用

International Conference on Wireless Information Networks and Systems

作者： Cheeneebash, Jayrani Rughooputh, Harry C. S. Univ Mauritius Dept Math Reduit Mauritius Univ Mauritius Dept Elect & Elect Engn Reduit Mauritius

ISBN: (纸本)9728865651

The channel assignment problem in cellular radio networks is known to belong to the class of NP-complete optimisation problems. In this paper we present a new algorithm to solve the Channel Assigninent Problem using estimation of distribution algorithm. The convergence rate of this new method is shown to be very much faster than other methods such as simulated annealing, neural networks and genetic algorithm.

关键词： channel assignment problem estimation of distribution algorithm

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A Novel Hybrid Differential Evolution-estimation of distribution algorithm for Dynamic Optimization Problem

A Novel Hybrid Differential Evolution-Estimation of Distribu...

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IEEE Congress on Evolutionary Computation

作者： Song, Xiangman Tang, Lixin Northeastern Univ Logist Inst Shenyang Peoples R China

ISBN: (纸本)9781479904549;9781479904532

In many engineering applications, the dynamic optimization problems with Ordinary Differential Equations (ODE) or Differential Algebraic Equations (DAE) constraints are encountered frequently. These types of problems are solved difficultly because of the characteristic of their nonlinear, multidimensional and multimodal. In this paper, a novel hybrid Differential Evolution (DE) and estimation of distribution algorithm (EDA) is proposed for the dynamic optimization problems. A novel hybrid scheme based on DE and EDA (DE-EDA) is designed to generate the offspring population. Using the DE-EDA, the population can reach a promising area in which the optimal solution is located speedily. A modified mutation scheme is proposed which can increase the diversity of the population. In addition, the modeling and sampling scheme based on empirical Copula is used to improve the speed of modeling and sampling. Eight optimal control optimization problems and one parameter estimation problem are tested to measure the performance of the algorithm. Experimental results show that the algorithm is feasible and effective.

关键词： dynamic optimization estimation of distribution algorithm differential evolution empirical Copula

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Hyper-heuristic Three-Dimensional estimation of distribution algorithm for Distributed Assembly Permutation Flowshop Scheduling Problem 19th

Hyper-heuristic Three-Dimensional Estimation of Distribution...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Li, Xiao Zhang, Zi-Qi Hu, Rong Qian, Bin Li, Kun 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

ISBN: (纸本)9789819947546;9789819947553

For the distributed assembly permutation flowshop scheduling problem (DAPFSP) to minimize the maximum completion time, this study suggests a hyperheuristic three-dimensional estimation of distribution algorithm (HH3DEDA) for solving it. The HH3DEDA consists of a high-level strategy (HLS) domain and a low-level problem (LLP) domain. The HLS domain guides the global search direction of the algorithm, while the LLP domain is responsible for searching local information in the problem domain. The HH3DEDA in this paper uses a variety of optimization strategies and metaheuristics that allow for global search and optimization, with the simultaneous setting up of nine variable neighborhood local search operations, and the arrangement of them as HLS domain individuals. Concurrently, the three-dimensional distribution estimation algorithm (3DEDA) is used in the HLS domain to learn the block structure of high-quality individuals in the HLS domain and their location information., and generating new HLS domain individuals by sampling the probability model in 3DEDA, then a series of ordered heuristic operators represented by each new individual generated at the HLS domain is used as a new heuristic algorithm at the LLP domain to perform a more in-deep neighborhood search in the problem domain.

关键词： Distributed Assembly Hyper-heuristic algorithm estimation of distribution algorithm Variable neighborhood local search

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Redundancy Allocation problem for a Series-Parallel system using estimation of distribution algorithm

Redundancy Allocation problem for a Series-Parallel system u...

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World Automation Congress (WAC)

作者： Melo, Haydee Junzo, Watada Waseda Univ Grad Sch Prod Informat & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9781467344975

Reliability is an engineering field that recently has captivated the attention of researches. Its goal is to develop new techniques to improve the security and performance of the systems. The increasing complexity in the systems as a result of growing technology makes them more susceptible for failures. In the redundancy allocation problem (RAP) its principal objective is to maximize the availability while reducing the cost, volume or weight of the system. In this research an estimation-of-distribution algorithm (EDA) approach is proposed for solving the redundancy allocation problem for a series-parallel system.

关键词： Reliability Availability Maintainability Redundancy estimation of distribution algorithm

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Motion Generation of Multi-Legged Robot in Complex Terrains by using estimation of distribution algorithm

Motion Generation of Multi-Legged Robot in Complex Terrains ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Jiang, Min Huang, Zhongqiang Jiang, Guiying Shi, Minghui Zeng, Xiangxiang Xiamen Univ Dept Cognit Sci Xiamen Peoples R China Xiamen Univ Fujian Key Lab Brain Inspired Comp Tech & Applica Xiamen Peoples R China Xiamen Univ Dept Comp Sci Xiamen Peoples R China Xiamen Univ Coll Foreign Languages Xiamen Peoples R China Sangfor Technol Co Shenzhen Peoples R China

ISBN: (纸本)9781538627266

Motion generation is one of the most important and challenging problems in multi-legged robot research. Most of the existing methods show a good fulfillment of the requirements of robots in structured environments. However, it still faces many challenges to generate motions effectively and quickly for multi-legged robot works in complex environments. In this paper, we put forward a method which converts the motion generation problem into a Multi-objective Optimization Problem (MOP), which will make the robot not only run as fast as possible, but also save energy, and then use a distribution estimation algorithm, the trend prediction model method, to obtain motions for a six-legged robot. Experiments show that this method is effective.

关键词： Robotics Multi-legged Robot Motion Generation estimation of distribution algorithm

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Reinforcement Learning-Based estimation of distribution algorithm for Energy-Efficient Distributed Heterogeneous Flexible Job Shop Scheduling Problem 20th

Reinforcement Learning-Based Estimation of Distribution Algo...

引用

20th International Conference on Intelligent Computing (ICIC)

作者： Zhao, Fuqing Li, Mengjie Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China

ISBN: (纸本)9789819755776;9789819755783

The energy-efficient distributed heterogeneous flexible job shop scheduling problem (DHFJSP), incorporating green objectives and multi-factory production models, is a widespread but challenging problem in the manufacturing industry. A reinforcement learning-based estimation of distribution algorithm (RLEDA) is proposed to solve the energy-efficient DHFJSP while minimizing the makespan and total energy consumption (TEC). A hybrid heuristic initialization method is devised to obtain a high-quality population. Two probabilistic models are employed to generate new solutions based on the characteristics of the sub-problems to avoid premature convergence. The Q-learning-based population learning rate adaptive mechanism adjusts the degree of learning information from dominant individuals to improve the distribution of the population. Thirty instances of different scales are utilized to evaluate the effectiveness of the RLEDA. The experimental results show that the RLEDA outperforms the comparison algorithms in solving energy-efficient DHFJSP.

关键词： estimation of distribution algorithm Reinforcement learning Flexible job shop scheduling problem Energy efficiency Distributed heterogeneous scheduling

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Inference of Genetic Regulatory Networks Using an estimation of distribution algorithm

Inference of Genetic Regulatory Networks Using an Estimation...

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8th Brazilian Symposium on Bioinformatics (BSB)

作者： Salva, Thyago Emmendorfer, Leonardo R. Werhli, Adriano V. Univ Fed Rio Grande FURG Programa Posgrad Comp Ctr Ciencias Computac C3 Rio Grande Brazil

ISBN: (纸本)9783319026237;9783319026244

Inference of Genetic Regulatory Networks from sparse and noisy expression data is still a challenge nowadays. In this work we use an estimation of distribution algorithm to infer Genetic Regulatory Networks. In order to evaluate the algorithm we apply it to three types of data: (i) data simulated from a multivariate Gaussian distribution, (ii) data simulated from a realistic simulator, GeneNetWeaver and (iii) data from flow cytometry experiments. The proposed inference method shows a performance comparable with traditional inference algorithms in terms of the network reconstruction accuracy.

关键词： Genetic Regulatory Networks estimation of distribution algorithm Bayesian Networks

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Effect of Model Complexity for estimation of distribution algorithm in NK Landscapes

Effect of Model Complexity for Estimation of Distribution Al...

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IEEE Symposium on Foundations of Computational Intelligence (FOCI)

作者： Liaw, Rung-Tzuo Ting, Chuan-Kang Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 621 Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Chiayi 621 Taiwan

ISBN: (纸本)9781467359016

Evolutionary algorithms (EAs) have been widely proved to be effective in solving complex problems. estimation of distribution algorithm (EDA) is an emerging EA, which manipulates probability models instead of genes for evolution EDA creates probability models based on the promising solution in the population and generates offspring by sampling from these models. The model complexity is a key factor in the performance of EDA. Complex models can express the relations among variables more accurately than simple models. However, for some problems with strong interaction among variables, building a model for all the relations becomes unrealistic and impractical due to its high computational cost and requirement for a large population size. This study aims to understand the behaviors of EDAs with different model complexities in NK landscapes. Specifically, this study compares the solution quality and convergence speed of univariate marginal distribution algorithm (UMDA), bivariate marginal distribution algorithm (BMDA), and estimation of Bayesian network (EBNA) in the NK landscapes with different parameter settings. The comparative results reveal that high complexity does not imply high performance: Simple model such as UMDA and BMDA can outperform complex mode like EBNA on the tested NK landscape problems. The results also show that BMDA achieves a stable high probability of generating the best solution and satisfactory solution quality;by contrast, the probability for EBNA drastically declines after some generations.

关键词： estimation of distribution algorithm model complexity NK landscapes probability model

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Hybrid estimation of distribution algorithm for solving a Resource Level Allocation Problem in a Legal Business 19

Hybrid Estimation of Distribution Algorithm for solving a Re...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Ayodele, Mayowa Papamichail, K. Nadia Gallagher, Geraldine Buckley, Darren Univ Manchester Manchester Lancs England DWF Law LLP Manchester Lancs England

ISBN: (纸本)9781450367486

Resource level allocation entails assigning execution times to a set of resource levels required to complete a task. This is an important problem, particularly in the services sector. We consider a real-world variant of this problem originating from a legal business. The objective considered is the maximisation of damages savings. We apply an estimation of distribution algorithm (EDA) to this problem and use a machine learning model, Random Forest, as a fitness approximation method. The hybrid EDA presents promising results.

关键词： estimation of distribution algorithm Genetic algorithm Machine Learning Fitness Approximation Insurance Legal Business Random Forest Resource Level Allocation

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An estimation of distribution algorithm for the 3D Bin Packing Problem with Various Bin Sizes

An Estimation of Distribution Algorithm for the 3D Bin Packi...

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14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Cai, Yaxiong Chen, Huaping Xu, Rui Shao, Hao Li, Xueping Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Shanghai Univ Int Business & Econom Sch WTO Res & Educ Shanghai 200336 Peoples R China Univ Tennessee Dept Ind & Informat Engn Knoxville TN 37996 USA

ISBN: (纸本)9783642412783;9783642412776

The 3D bin packing problem (3DBPP) is a practical problem modeled from modern industry application such as container ship loading and plane cargo management. Unlike traditional bin packing problem where all bins are of the same size, this paper investigates a more general type of 3DBPP with bins of various sizes. We proposed a modified univariate marginal distribution algorithm (UMDA) for solving the problem. A packing strategy derived from a deepest bottom left packing method was employed. The modified UMDA was experimentally compared with CPLEX and a genetic algorithm (GA) approach. The experimental study showed that the proposed algorithm performed better than GA and CPLEX for large-scale instances.

关键词： Optimization Bin packing problem estimation of distribution algorithm Genetic algorithm

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