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

共 435 条记录，以下是121-130 订阅

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An estimation of distribution algorithm with Efficient Constructive Repair/Improvement Operator for the Dynamic Weapon-Target Assignment

An Estimation of Distribution Algorithm with Efficient Const...

引用

31st Chinese Control Conference

作者： Xin Bin Chen Jie Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Univ Manchester Manchester Business Sch Decis & Cognit Sci Res Ctr Manchester M15 6PB Lancs England Minist Educ Key Lab Complex Syst Intelligent Control & Decis Beijing 100081 Peoples R China

ISBN: (纸本)9789881563811

This paper reports our recent research about new efficient problem-solvers for the dynamic weapon-target assignment (DWTA). A binary-encoding-based estimation of distribution algorithm (EDA) is proposed to solve DWTA problems. An elaborate constructive repair/improvement (CRI) operator is proposed and integrated into the EDA to achieve constraint saturation, which conduces to constraint satisfaction as well as the improvement of generated solutions. The performance comparison against another two EDAs which employ well-known constraint handling methods demonstrates the superiority of the CRI operator. The proposed EDA based on the CRI operator also shows very competitive and even better performance against several state-of-the-art DWTA algorithms.

关键词： Dynamic weapon-target assignment estimation of distribution algorithm constructive repair/improvement operator constraint handling

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MP-EDA: A Robust estimation of distribution algorithm with Multiple Probabilistic Models for Global Continuous Optimization

MP-EDA: A Robust Estimation of Distribution Algorithm with M...

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8th International Conference of Simulated Evolution and Learning

作者： Zhong, Jing-hui Zhang, Jun Fan, Zhun Sun Yat Sen Univ Dept Comp Sci Key Lab Digital Life Minist EducKey Lab Software TechnolEduc Dept Gu Guangzhou Peoples R China Tech Univ Denmark Copenhagen Denmark

ISBN: (纸本)9783642172977

Extending estimation of distribution algorithms (EDAs) to the continuous field is a promising and challenging task. With a single probabilistic model, most existing continuous EDAs usually suffer from the local stagnation or a low convergence speed. This paper presents an enhanced continuous EDA with multiple probabilistic models (MP-EDA). In the MP-EDA, the population is divided into two subpopulations. The one involved by histogram model is used to roughly capture the global optima, whereas the other involved by Gaussian model is aimed at finding highly accurate solutions. During the evolution, a migration operation is periodically carried out to exchange some best individuals of the two subpopulations. Besides, the MP-EDA adaptively adjusts the offspring size of each subpopulation to improve the searching efficiency. The effectiveness of the MP-EDA is investigated by testing ten benchmark functions. Compared with several state-of-the-art evolutionary computations, the proposed algorithm can obtain better results in most test cases.

关键词： estimation of distribution algorithm Evolutionary Computation Histogram Multivariate Gaussian distribution Global Optimization

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A Hybrid estimation of distribution algorithm for Solving the Multi-objective Multiple Traveling Salesman Problem

A Hybrid Estimation of Distribution Algorithm for Solving th...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Shim, V. A. Tan, K. C. Tan, K. K. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

ISBN: (纸本)9781467315098

The multi-objective multiple traveling salesman problem (MmTSP) is a generalization of the classical multi-objective traveling salesman problem. In this paper, a formulation of the MmTSP, which considers the weighted sum of the total traveling costs of all salesmen and the highest traveling cost of any single salesman, is proposed. An estimation of distribution algorithm (EDA) based on restricted Boltzmann machine is used for solving the formulated problem. The EDA is developed in the decomposition framework of multi-objective optimization. Due to the limitation of EDAs in generating a wide range of solutions, the EDA is hybridized with the evolutionary gradient search. Simulation studies are carried out to examine the optimization performances of the proposed algorithm on MmTSP with different number of objective functions, salesmen and problem sizes.

关键词： Decomposition estimation of distribution algorithm evolutionary gradient search hybrid multi-objective optimization multiple traveling salesman problem restricted Boltzmann machine

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A Multi-model estimation of distribution algorithm for Agent Routing Problem in Multi-point Dynamic Task 37

A Multi-model Estimation of Distribution Algorithm for Agent...

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37th Chinese Control Conference (CCC)

作者： Lu, Sai Xin, Bin Dou, Lihua Wang, Ling Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Tsinghua Univ Key Lab Intelligent Control & Decis Complex Syst Beijing 100084 Peoples R China Tsinghua Univ Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100084 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9789881563958

The agent routing problem in multi-point dynamic task (ARP-MPDT) is a multi-task routing problem of a mobile agent. In this problem, there are multiple tasks to be carried out in different locations. As time goes on, the state of each task will change nonlinearly. The agent must go to the task points in turn to perform the tasks, and the execution time of each task is related to the state of the task point when the agent arrives at the point. ARP-MPDT is a typical NP-hard optimization problem. In this paper, we establish the nonlinear ARP-MPDT model. A multi-model estimation of distribution algorithm (EDA) employing node histogram models (NHM) and edge histogram models (EHM) in probability modeling is used to solve the ARP-MPDT. The selection ratio of NHM and EHM probability models is adjusted adaptively. Finally, performance of the algorithm for solving the ARP-MPDT problem is verified by the computational experiments.

关键词： estimation of distribution algorithm Multi-model Routing Multi-point dynamic task

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Configuration Design of Mechanical Assemblies using an estimation of distribution algorithm and Constraint Programming

Configuration Design of Mechanical Assemblies using an Estim...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Cheong, Hyunmin Ebrahimi, Mehran Butscher, Adrian Iorio, Francesco Autodesk Res Toronto ON Canada

ISBN: (纸本)9781728121536

A configuration design problem in mechanical engineering involves finding an optimal assembly of components and joints that realizes some desired performance criteria. Such a problem is a discrete, constrained, and black-box optimization problem. A novel method is developed to solve the problem by applying Bivariate Marginal distribution algorithm (BMDA) and constraint programming (CP). BMDA is a type of estimation of distribution algorithm (EDA) that exploits the dependency knowledge learned between design variables without requiring too many fitness evaluations, which tend to be expensive for the current application. BMDA is extended with adaptive chi-square testing to identify dependencies and Gibbs sampling to generate new solutions. Also, repair operations based on CP are used to deal with infeasible solutions found during search. The method is applied to a vehicle suspension design problem and is found to be more effective in converging to good solutions than a genetic algorithm and other EDAs. These contributions are significant steps towards solving the difficult problem of configuration design in mechanical engineering with evolutionary computation.

关键词： configuration design mechanical assemblies estimation of distribution algorithm constraint programming

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An estimation of distribution algorithm for Puzzle Reconstruction Problem 17th

An Estimation of Distribution Algorithm for Puzzle Reconstru...

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17th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) / 14th International Conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Qin, Bin Tseng, Shih-Pang Changzhou Inst Ind Technol Modern Equipment Mfg Coll Changzhou Jiangsu Peoples R China Changzhou Coll Informat Technol Sch Software & Big Data 22 Mingxin Middle Rd Changzhou 213164 Jiangsu Peoples R China

ISBN: (纸本)9789811910531;9789811910524

Puzzle is a game to have a very long history for training the human logic thinking. In addition, the puzzle-solving methods can be used in various practical applications. In this paper, we proposed an EDA-based edge-matching puzzle solver. The proposed approach is based on the probability model. We have presented how to build the suitable probability model for puzzle solving. And we also provide the sampling method to construct a candidate solution. The experimental results show the proposed approach is available and effective.

关键词： estimation of distribution algorithm Meta-heuristics Puzzle solving

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Real-coded estimation of distribution algorithm by Using Probabilistic Models with Multiple Learning Rates

Real-coded Estimation of Distribution Algorithm by Using Pro...

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International Conference on Computational Science (ICCS) on the Ascent of Computational Excellence

作者： Nakao, Masahiro Hiroyasu, Tomoyuki Miki, Mitsunori Yokouchi, Hisatake Yoshimi, Masato Univ Tsukuba Ctr Computat Sci Ibaraki Japan Doshisha Univ Dept Life & Med SCi Kyoto Japan Doshisha Univ Fac Sci & Engn Kyoto Japan

Here, a new Real-coded estimation of distribution algorithm (EDA) is proposed. The proposed EDA is called Real-coded EDA using Multiple Probabilistic Models (RMM). RMM includes multiple types of probabilistic models with different learning rates and diversities. The search capability of RMM was examined through several types of continuous test function. The results indicated that the search capability of RMM is better than or equivalent to that of existing Real-coded EDAs. Since better searching points are distributed for other probabilistic models positively, RMM can discover the global optimum in the early stages of the search.

关键词： estimation of distribution algorithm optimization continuous function

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Quantum-Inspired estimation of distribution algorithm To Solve The Travelling Salesman Problem

Quantum-Inspired Estimation Of Distribution Algorithm To Sol...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Soloviev, Vicente P. Bielza, Concha Larranaga, Pedro Univ Politecn Madrid Computat Intelligence Grp Madrid Spain

ISBN: (纸本)9781728183923

A novel Quantum-Inspired estimation of distribution algorithm (QIEDA) is proposed to solve the Travelling Salesman Problem (TSP). The QIEDA uses a modified version of the W state quantum circuits to sample new solutions during the algorithm runtime. The algorithm behaviour is compared with other state-of-the-art population-based algorithms. QIEDA convergence is faster than other algorithms, and the obtained solutions improve as the size of the problem increases. Moreover, we show that quantum noise enhances the search of an optimal solution. Because quantum computers differ from each other, partly due to the topology that distributes the qubits, the computational cost of executing the QIEDA in different topologies is analyzed and an ideal topology is proposed for the TSP solved with the QIEDA.

关键词： quantum computing quantum machine learning travelling salesman problem estimation of distribution algorithm

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A Hybrid estimation of distribution algorithm with Monarch Butterfly Optimization 27

A Hybrid Estimation of Distribution Algorithm with Monarch B...

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27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Zhu, Bo Jia, Li Li, Jianfang Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China

ISBN: (纸本)9798350349184;9798350349191

The complex optimization problems have been investigated deeply by researchers in the optimization community. The estimation of distribution algorithm (EDA) and the monarch butterfly optimization algorithm (MBO) are meta-heuristic algorithms that attracted wide attention. In this study, an improved algorithm based on estimation of distribution of algorithm combined with Monarch Butterfly Optimization algorithm named EDMBO is proposed. The weighted average of candidate solutions is embedded to estimate the mean value. A linear search strategy is introduced to enhance the exploitation of the algorithm. The CEC 2017 benchmark test suite is adopted to verify the performance of the algorithm. The experimental results show that the EDMBO is competitive.

关键词： hybrid algorithm estimation of distribution algorithm monarch butterfly optimization linear search

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A Cloud Computing Resource Scheduling Scheme Based on estimation of distribution algorithm 2

A Cloud Computing Resource Scheduling Scheme Based on Estima...

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2nd International Conference on Systems and Informatics (ICSAI)

作者： Chen, Niansheng Fang, Xiaoping Wang, Xin Shanghai Dianji Univ Elect & Informat Sch Shanghai Peoples R China Hubei Normal Univ Sch Comp Sci Huangshi Peoples R China

ISBN: (纸本)9781479954582

Resource scheduling is one of the key problems of cloud computing, no wonder, the scheduling policy and algorithm affect the performance of the cloud system directly. In order to improve the utilization of cloud computing resources and keep load balancing, a cloud computing resource scheduling algorithm based on estimation of distribution algorithm is proposed. In this algorithm, the idea of population based incremental learning(PBIL) algorithm is fully used. In this paper, cloud computing resource scheduling algorithm model is established firstly, and then objective solution is made by using the PBIL algorithm. Finally, the simulation analysis of algorithm performance is conducted. The simulation results show that the PBIL algorithm can take shorter time to complete task and achieve resource load balancing, especially, for the resource scheduling with large-scale task, the advantages are more apparent.

关键词： cloud computing resource scheduling estimation of distribution algorithm PBIL algorithm

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