检索结果-内蒙古大学图书馆

A hybrid estimation of distribution algorithm for the vehicle routing problem with time windows

COMPUTERS & INDUSTRIAL ENGINEERING 2019年 130卷 75-96页

作者： Perez-Rodriguez, Ricardo Hernandez-Aguirre, Arturo CONACYT CIMAT AC Barrio Estn Bartolome de las Casas 314 Aguascalientes 20259 Ags Mexico CIMAT AC Jalisco S-N Guanajuato 36240 Gto Mexico

The Vehicle Routing Problem (VRP) seeks to find minimum-travel routes for a set of vehicles. The routes contain a set of customers with known demands. Each vehicle departs and arrives at the same depot. In the vehicle routing problem with time windows (VRPTW), each vehicle has to arrive in a specific time window with each customer and also each vehicle has to return to the depot before a due time. In this research, the use of an estimation of distribution algorithm to solve the problem is proposed. The algorithm uses the generalized Mallows distribution as a probability model to describe the distribution of the solution space. Homberger-Gehring's instances are used as input and test parameters in order to show that the modification of the generalized Mallows distribution mentioned is able to produce competitive sequences for the VRPTW against some other estimation of distribution algorithms used in permutation-based optimization problems.

关键词： estimation of distribution algorithm Mallows distribution Vehicle routing problem Combinatorial optimization

来源：评论

学校读者我要写书评

暂无评论

Improved Alopex-based evolutionary algorithm by Gaussian copula estimation of distribution algorithm and its application to the Butterworth filter design

引用

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2018年第1期49卷 160-178页

作者： Yang, Yihang Cheng, Xiang Cheng, Junrui Jiang, Da Li, Shaojun East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Shanghai Peoples R China

The application of evolutionary algorithms (EAs) is becoming widespread in engineering optimisation problems because of their simplicity and effectiveness. The Alopex-based evolutionary algorithm (AEA) possesses the basic characteristics of heuristic search algorithms but is lacking in adequate information about the fitness landscape of the input domain, reducing the convergence speed. To improve the performance of AEA, the Gaussian copula estimation of distribution algorithm (EDA) is embedded into the original AEA in this paper. With the help of Gaussian copula EDA, precise probability models are built utilising the best solutions, which can increase the convergence speed, and at the same time, keep the population diversity as much as possible. The simulation results on the benchmark functions and the application to the Butterworth filter design demonstrate the efficiency and effectiveness of the proposed algorithm, compared with several other EAs.

关键词： Evolutionary algorithm Alopex Gaussian copula estimation of distribution algorithm

来源：评论

学校读者我要写书评

暂无评论

Knowledge-Based Reinforcement Learning and estimation of distribution algorithm for Flexible Job Shop Scheduling Problem

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第4期7卷 1036-1050页

作者： Du, Yu Li, Jun-qing Chen, Xiao-long Duan, Pei-yong Pan, Quan-ke Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Yantai Univ Sch Math & Informat Sci Yantai 264005 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

In this study, a flexible job shop scheduling problem with time-of-use electricity price constraint is considered. The problem includes machine processing speed, setup time, idle time, and the transportation time between machines. Both maximum completion time and total electricity price are optimized simultaneously. A hybrid multi-objective optimization algorithm of estimation of distribution algorithm and deep Q-network is proposed to solve this. The processing sequence, machine assignment, and processing speed assignment are all described using a three-dimensional solution representation. Two knowledge-based initialization strategies are designed for better performance. In the estimation of distribution algorithm component, three probability matrices corresponding to solution representation are provided. In the deep Q-network component, 34 state features are selected to describe the scheduling situation, while nine knowledge-based actions are defined to refine the scheduling solution, and the reward based on the two objectives is designed. As the knowledge for initialization and optimization strategies, five properties of the considered problem are proposed. The proposed mixed integer linear programming model of the problem is validated by exact solver CPLEX. The results of the numerical testing on wide-range scale instances show that the proposed hybrid algorithm is efficient and effective at solving the integrated flexible job shop scheduling problem.

关键词： Transportation Optimization Job shop scheduling Standards Knowledge based systems Indexes estimation Flexible job shop scheduling problem deep reinforcement learning estimation of distribution algorithm multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Maximizing Lifetime of Range-Adjustable Wireless Sensor Networks: A Neighborhood-Based estimation of distribution algorithm

引用

IEEE TRANSACTIONS ON CYBERNETICS 2021年第11期51卷 5433-5444页

作者： Chen, Zong-Gan Lin, Ying Gong, Yue-Jiao Zhan, Zhi-Hui Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Peoples R China Sun Yat Sen Univ Dept Psychol Guangzhou 510006 Peoples R China Victoria Univ Melbourne Vic 8001 Australia

Sensor activity scheduling is critical for prolonging the lifetime of wireless sensor networks (WSNs). However, most existing methods assume sensors to have one fixed sensing range. Prevalence of sensors with adjustable sensing ranges posts two new challenges to the topic: 1) expanded search space, due to the rise in the number of possible activation modes and 2) more complex energy allocation, as the sensors differ in the energy consumption rate when using different sensing ranges. These two challenges make it hard to directly solve the lifetime maximization problem of WSNs with range-adjustable sensors (LM-RASs). This article proposes a neighborhood-based estimation of distribution algorithm (NEDA) to address it in a recursive manner. In NEDA, each individual represents a coverage scheme in which the sensors are selectively activated to monitor all the targets. A linear programming (LP) model is built to assign activation time to the schemes in the population so that their sum, the network lifetime, can be maximized conditioned on the current population. Using the activation time derived from LP as individual fitness, the NEDA is driven to seek coverage schemes promising for prolonging the network lifetime. The network lifetime is thus optimized by repeating the steps of the coverage scheme evolution and LP model solving. To encourage the search for diverse coverage schemes, a neighborhood sampling strategy is introduced. Besides, a heuristic repair strategy is designed to fine-tune the existing schemes for further improving the search efficiency. Experimental results on WSNs of different scales show that NEDA outperforms state-of-the-art approaches. It is also expected that NEDA can serve as a potential framework for solving other flexible LP problems that share the same structure with LM-RAS.

关键词： Wireless sensor networks Sensors Sociology Statistics Batteries Energy consumption Resource management estimation of distribution algorithm lifetime maximization neighborhood wireless sensor network (WSN)

来源：评论

学校读者我要写书评

暂无评论

Dynamic deployment optimization of near space communication system using a novel estimation of distribution algorithm

引用

APPLIED SOFT COMPUTING 2019年 78卷 569-582页

作者： Wang, Zhao Gong, Maoguo Xidian Univ Key Lab Intelligent Percept & Image Understanding Int Res Ctr Intelligent Percept & Computat Minist Educ 2 South TaiBaiRd Xian 710071 Shaanxi Peoples R China

A robust deployment of the airship platforms is crucial to the performance of the Near Space Communication System (NSCS) in the dynamic environment. In this paper, a multiobjective NSCS deployment optimization model with multi-phased periodic user distribution is proposed. To optimize this model, we propose a local incremental estimation of distribution algorithm with an asymmetrical domination relationship within the multiobjective evolutionary algorithm based on decomposition framework. The conflict between the selection mechanism and the domination relationship is also analyzed theoretically for the first time. To obtain robust solutions under this conflict, the local distribution information of a certain subproblem within several generations is encompassed into a local incremental distribution model. As a generalized form of the existing domination relationship, an asymmetrical domination relationship (ADR), which treats the current and past objective values differently, is proposed to select robust solutions. The proposed algorithm is also tested on four designed problems compared with another four popular algorithms and proves its superiority. Some important parameters are also investigated in the experiments and some guidelines on tuning these parameters are given as well. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Near space communication system estimation of distribution algorithm Multiobjective optimization Domination relationship

来源：评论

学校读者我要写书评

暂无评论

An inverse model-based multiobjective estimation of distribution algorithm using Random-Forest variable importance methods

引用

COMPUTATIONAL INTELLIGENCE 2022年第3期38卷 1018-1056页

作者： Gholamnezhad, Pezhman Broumandnia, Ali Seydi, Vahid Islamic Azad Univ Dept Comp South Tehran Branch Ahang StBlv South Nabard Tehran Iran

Most existing methods of multiobjective estimation of distributed algorithms apply the estimation of distribution of the Pareto-solution on the decision space during the search and little work has proposed on making a regression-model for representing the final solution set. Some inverse-model-based approaches were reported, such as inversed-model of multiobjective evolutionary algorithm (IM-MOEA), where an inverse functional mapping from Pareto-Front to Pareto-solution is constructed on nondominated solutions based on Gaussian process and random grouping technique. But some of the effective inverse models, during this process, may be removed. This paper proposes an inversed-model based on random forest framework. The main idea is to apply the process of random forest variable importance that determines some of the best assignment of decision variables (x(n)) to objective functions (f(m)) for constructing Gaussian process in inversed-models that map all nondominated solutions from the objective space to the decision space. In this work, three approaches have been used: classical permutation, Naive testing approach, and novel permutation variable importance. The proposed algorithm has been tested on the benchmark test suite for evolutionary algorithms [modified Deb K, Thiele L, Laumanns M, Zitzler E (DTLZ) and Walking Fish Group (WFG)] and indicates that the proposed method is a competitive and promising approach.

关键词： estimation of distribution algorithm Gaussian process inverse modeling multiobjective optimization random forest variable importance

来源：评论

学校读者我要写书评

暂无评论

A regularity model-based multiobjective estimation of distribution algorithm with reducing redundant cluster operator

引用

APPLIED SOFT COMPUTING 2012年第11期12卷 3526-3538页

作者： Wang, Yong Xiang, Jian Cai, Zixing Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Hunan Engn Lab Adv Control & Intelligent Automat Changsha 410083 Hunan Peoples R China

A regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) has been proposed for solving continuous multiobjective optimization problems with variable linkages. RM-MEDA is a kind of estimation of distribution algorithms and, therefore, modeling plays a critical role. In RM-MEDA, the population is split into several clusters to build the model. Moreover, the fixed number of clusters is recommended in RM-MEDA when solving different kinds of problems. However, based on our experiments, we find that the number of clusters is problem-dependent and has a significant effect on the performance of RM-MEDA. Motivated by the above observation, in this paper we improve the clustering process and propose a reducing redundant cluster operator (RRCO) to build more precise model during the evolution. By combining RRCO with RM-MEDA, we present an improved version of RM-MEDA, named IRM-MEDA. In this paper, we also construct four additional continuous multiobjective optimization test instances. The experimental results have shown that IRM-MEDA outperforms RM-MEDA in terms of efficiency and effectiveness. In particular, IRM-MEDA performs on average 31.67% faster than RM-MEDA. (c) 2012 Elsevier B.V. All rights reserved.

关键词： estimation of distribution algorithm Multiobjective optimization Modeling Reducing redundant cluster operator

来源：评论

学校读者我要写书评

暂无评论

Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm

引用

NEURAL COMPUTING & APPLICATIONS 2013年第1期22卷 11-20页

作者： Peralta Donate, Juan Li, Xiaodong Gutierrez Sanchez, German Sanchis de Miguel, Araceli Univ Carlos III Madrid Grp CAOS Dept Comp Sci Leganes 28911 Spain Royal Melbourne Inst Techonol Sch Comp Sci & Informat Technol Melbourne Vic Australia

Time series forecasting is an important tool to support both individual and organizational decisions (e.g. planning production resources). In recent years, a large literature has evolved on the use of evolutionary artificial neural networks (EANN) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified nonlinear relationship between time series variables. In this work, two new approaches of a previous system, automatic design of artificial neural networks (ADANN) applied to forecast time series, are tackled. In ADANN, the automatic process to design artificial neural networks was carried out by a genetic algorithm (GA). This paper evaluates three methods to evolve neural networks architectures, one carried out with genetic algorithm, a second one carried out with differential evolution algorithm (DE) and the last one using estimation of distribution algorithms (EDA). A comparative study among these three methods with a set of referenced time series will be shown. In this paper, we also compare ADANN forecasting ability against a forecasting tool called Forecast Pro(A (R)) (FP) software, using five benchmark time series. The object of this study is to try to improve the final forecasting getting an accurate system.

关键词： Evolutionary computation Genetic algorithms Differential evolution estimation of distribution algorithm Artificial neural networks Time series Forecasting

来源：评论

学校读者我要写书评

暂无评论

A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown

引用

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2016年第1期67卷 68-82页

作者： Wang, Kai Huang, Yun Qin, Hu Wuhan Univ Econ & Management Sch Wuhan 430072 Peoples R China Macau Univ Sci & Technol Sch Business Taipa 999078 Macau Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China

As the research interest in distributed scheduling is growing, distributed permutation flowshop scheduling problems (DPFSPs) have recently attracted an increasing attention. This paper presents a fuzzy logic-based hybrid estimation of distribution algorithm (FL-HEDA) to address DPFSPs under machine breakdown with makespan criterion. In order to explore more promising search space, FL-HEDA hybridises the probabilistic model of estimation of distribution algorithm with crossover and mutation operators of genetic algorithm to produce new offspring. In the FL-HEDA, a novel fuzzy logic-based adaptive evolution strategy (FL-AES) is adopted to preserve the population diversity by dynamically adjusting the ratio of offspring generated by the probabilistic model. Moreover, a discrete-event simulator that models the production process under machine breakdown is applied to evaluate expected makespan of offspring individuals. The simulation results show the effectiveness of FL-HEDA in solving DPFSPs under machine breakdown.

关键词： distributed permutation flowshop estimation of distribution algorithm fuzzy logic machine breakdown makespan

来源：评论

学校读者我要写书评

暂无评论

An insight to the performance of estimation of distribution algorithm for multiple line outage identification

引用

SWARM AND EVOLUTIONARY COMPUTATION 2018年 39卷 114-122页

作者： Ahmed, A. Khan, Q. Naeem, M. Iqbal, M. Anpalagan, A. Awais, M. COMSATS Inst Informat Technol Wah Campus Wah Cantonment Pakistan Pakistan & Ryerson Univ COMSATS Inst Informat Technol Wah Campus Toronto ON Canada Ryerson Univ Toronto ON Canada

Realtime information relating to line outages has significant importance to pre-empt against the power system blackouts. Realtime information can be obtained by using phasor measurement units (PMUs) facilitating the realtime synchronized observations of voltage and current phasors at buses being monitored. Different optimization formulations including but not limited to linear, integer, stochastic, mixed integer and NP hard combinatorial optimization have been used to manipulate these phasor measurements for the detection of line outages. Single and double line outages can be addressed using combinatorial optimization but these are infeasible to apply for the detection of multiple line outages as the increased number of lines increases computational complexity. To alleviate the exponentially increased complexities of these combinatorial optimization problems, while investigating for multiple line outage, evolutionary, estimation of distribution algorithm is used. This method gives near optimal solution in which computational complexity and time is reduced efficiently. In this paper we scrutinize the use of phasor angle measurements to detect multiple power line outages. The proposed EDA is compared with binary particle swarm optimization (BPSO) algorithm, adaptive BPSO and genetic algorithm (GA) in terms of line outage detection performance, fitness convergence w.r.t. iterations and time consumption. The simulation results depict that the proposed EDA outperforms the other state of the art algorithms.

关键词： estimation of distribution algorithm Multiple Line Outage Detection Smart Grid

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：