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A hybrid differential evolution algorithm with estimation of distribution algorithm for reentrant hybrid flow shop scheduling problem

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NEURAL COMPUTING & APPLICATIONS 2018年第1期30卷 193-209页

作者： Zhou, Bing-hai Hu, Li-man Zhong, Zhen-yi Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

This paper proposes a reentrant hybrid flow shop scheduling problem where inspection and repair operations are carried out as soon as a layer has completed fabrication. Firstly, a scheduling problem domain of reentrant hybrid flow shop is described, and then, a mathematical programming model is constructed with an objective of minimizing total weighted completion time. Then, a hybrid differential evolution (DE) algorithm with estimation of distribution algorithm using an ensemble model (eEDA), named DE-eEDA, is proposed to solve the problem. DE-eEDA incorporates the global statistical information collected from an ensemble probability model into DE. Finally, simulation experiments of different problem scales are carried out to analyze the proposed algorithm. Results indicate that the proposed algorithm can obtain satisfactory solutions within a short time.

关键词： Reentrant hybrid flow shop Scheduling Differential evolution algorithm estimation of distribution algorithm

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

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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

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

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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

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

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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)

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A multi-model estimation of distribution algorithm for energy efficient scheduling under cloud computing system

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2018年 117卷 63-72页

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

How to manage the applications under computing systems such as a cloud computing system in a more efficient way is a focus problem. The primary performance goal is to reduce the execution time (makespan) of the application. As the need to cloud computing grows, the environmental influence of data centers attracts much attention. This paper aims at the scheduling of the precedence-constrained parallel application to minimize time and energy consumption efficiently. A multi-model estimation of distribution (mEDA) algorithm is adopted to determine both task processing permutation and voltage supply levels (VSLs). Specific operators to decrease execution time and energy consumption are designed. An improvement operator is also designed to enhance the diversity of the non-dominated solutions. The proposed algorithm is compared with the standard heuristic methods and a parallel bi-objective genetic algorithm (bGA). The comparative results show the Pareto solution set by the proposed algorithm is able to dominate a large proportion of those solutions by both the heuristic methods and the bGA. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Task graph scheduling Energy efficient scheduling Cloud computing estimation of distribution algorithm Precedence-constrained parallel application

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

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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

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

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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

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

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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

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

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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

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

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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

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