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Knowledge-based multi-objective estimation of distribution algorithm for solving reliability constrained cloud workflow scheduling

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第2期27卷 1401-1419页

作者： Li, Ming Pi, Dechang Qin, Shuo Nanjing Univ Aeronaut & Astronaut Sch Comp Sci & Technol Shengtai West Rd Nanjing 211100 Peoples R China

With the rapid development of cloud computing, numerous large-scale workflow are executed in the cloud environment. Therefore, the workflow scheduling in cloud environment has become an emerging topic. This paper focuses on a reliability constrained multi-objective workflow scheduling problem (RCMOWSP) with the objectives of minimum execution cost and time. To solve the RCMOWSP, this paper proposes a knowledge-based multi-objective estimation of distribution algorithm (KMOEDA) with several problem-specific operators. First, an idle time-based decoding scheme is applied to sort the permutation of tasks greedily. In the global search strategy, a probability model is constructed to improve the diversity of population. Based on the problem-specific knowledge, a reliability-aware local search strategy is designed to performs local search around the solutions that violate reliability constraint. An elite enhancement strategy with a task perturbation operator and a resource perturbation operator is introduced to further improve the elite non-dominated solutions in the external archive. A comprehensive experiment is conducted to verify the performance of KMOEDA. The comparative results show that the KMOEDA significantly outperforms several relative multi-objective workflow scheduling approaches in solving the RCMOWSP.

关键词： Cloud computing Workflow scheduling Multi-objective optimization estimation of distribution algorithm Reliability constraint

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Bound-guided hybrid estimation of distribution algorithm for energy-efficient robotic assembly line balancing

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 146卷 106604-106604页

作者： Sun, Bin-qi Wang, Ling Peng, Zhi-ping Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Guangdong Univ Petrochem Technol Sch Comp Maoming 525000 Peoples R China

Under the pressure of climate change, energy-efficient manufacturing has attracted much attention. Robotic assembly lines are widely-used in automotive and electronic manufacturing. It is necessary to consider the energy saving and economic criteria simultaneously when robots are utilized to operate assembly tasks replacing human labor. This paper addresses an energy-efficient robotic assembly line balancing (EERALB) problem with the criteria to minimize both the cycle time and total energy consumption. We present a multi-objective mathematical model and propose a bound-guided hybrid estimation of distribution algorithm to solve the problem. When designing the optimization algorithm, we adopt estimation of distribution algorithm (EDA) to tackle the task assignment, and design a non-dominated robot allocation (NGRA) heuristic which is embedded into the EDA to allocate suitable robot to each workstation. Moreover, we propose a bound-guided sampling (BGS) method, which is able to reduce the search space of EDA and focus the search on the promising area. The computational complexity of the proposed algorithm is analyzed and the effectiveness of the proposed NGRA and BGS is tested. In addition, we compare the performances of the proposed mathematical model and the proposed algorithm with those of the existing model and algorithms on a set of widely-used benchmark instances. Comparative results demonstrate the effectiveness of the proposed model and algorithm.

关键词： estimation of distribution algorithm Bound-guided sampling Energy-efficient robotic assembly line balancing Non-dominated robot allocation

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An estimation of distribution algorithm coupled with the generalized Mallows distribution for a school bus routing problem with bus stop selection

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REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL 2017年第3期14卷 288-298页

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

Although the estimation of distribution algorithms were originally designed for solving integer or real-valued domains, this contribution applies the algorithms mentioned to deal with a permutation-based problem, called school bus routing problem with bus stop selection, using the generalized Mallows distribution as an attempt to describe and obtain an explicit probability distribution over a set of school bus routes. In addition, a mutation operator is considered for improving the estimation of the central permutation, a parameter of the Mallows distribution. Different and diverse instances served as input and test parameters in order to show that permutation-based optimization problems such as the school bus routing problem with bus stop selection can be solved by means of a probability model, and improving the estimation of the central permutation helps the performance of the algorithm.

关键词： estimation of distribution algorithm Mallows distribution vehicle routing problem school bus routing problem

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Improving the estimation of distribution algorithm with a differential mutation for multilevel thresholding image segmentation

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第10期15卷 4255-4323页

作者： Ramos-Frutos, Jorge Armando Miguel-Andres, Israel Oliva, Diego Casas-Ordaz, Angel Ctr Innovac Aplicada Tecnol Competit Leon 37545 Guanajuato Mexico Univ Guadalajara Dept Ingn Electrofoton CUCEI Guadalajara 44430 Jalisco Mexico

Image segmentation consists of separating an image into regions that are entirely different from each other, and multilevel thresholding is a method used to perform this task. This article proposes an estimation of distribution algorithms (EDA) combined with a Differential Evolution (DE) operator as a metaheuristic to solve the multilevel thresholding problem. The proposal is called the Differential Mutation estimation of distribution algorithm (DMEDA), where the inclusion of the Differential Mutation increases the standard EDA's exploration capacity. The performance of the DMEDA for image segmentation is tested using Otsu's between-class variance and Kapur's entropy as objective functions applied separately over the Berkeley Segmentation Data Set 300 (BSDS300). Besides, a comparative study includes eight well-known algorithms in the literature. In this sense, statistical and non-parametric tests are performed to verify the efficiency of the DMEDA in solving the image segmentation problem from an optimization perspective. In terms of segmentation, different metrics are employed to verify the capabilities of the DMEDA to segment digital images properly. Regarding the two objective functions, the proposed DMEDA obtains better results in 97% of the experiments for Otsu's between-class variance and 85% for Kapur's entropy.

关键词： Image segmentation Multilevel thresholding estimation of distribution algorithm Differential mutation

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An estimation of distribution algorithm for hybrid flow shop scheduling under stochastic processing times

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第24期52卷 7360-7376页

作者： Wang, K. Choi, S. H. Qin, H. Wuhan Univ Dept Management Sci & Engn Econ & Management Sch Wuhan 430072 Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Pokfulam Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China

The estimation of distribution algorithm (EDA) has recently emerged as a promising alternative to traditional evolutionary algorithms for solving combinatorial optimisation problems. This paper presents a novel two-phase simulation-based EDA (TPSB-EDA) for minimising the makespan of a hybrid flow shop under stochastic processing times. To address the stochastic scheduling problem efficiently, the proposed TPSB-EDA incorporates a two-phase simulation model to estimate the performance of candidate solutions. In this model, an optimal back propagation network is firstly applied to identify a set of roughly good solutions, and then the selected solutions are further evaluated by a discrete-event simulation algorithm. Moreover, an annealing selection mechanism (ASM) is adopted to preserve the population diversity of EDA. Different from the selection operators of common EDAs, the ASM uses Boltzmann probability in the annealing algorithm to select part of population to establish the probabilistic model. Computation results indicate that the TPSB-EDA provides good solutions in the aspects of solution quality and computational efficiency.

关键词： annealing selection mechanism two-phase simulation model stochastic processing times hybrid flow shop estimation of distribution algorithm back propagation network

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Reduction of carbon emissions and project makespan by a Pareto-based estimation of distribution algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2015年 164卷 421-432页

作者： Zheng, Huan-yu Wang, Ling Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

Due to the increasing concerns about global warming, low-carbon production has been a hot topic around the world. In this paper, carbon emissions reduction and project makespan minimization are considered simultaneously. To formulate the problem, a multi-objective multi-mode resource-constrained project scheduling model with makespan and carbon emissions criteria is given. To solve the problem, a Pareto-based estimation of distribution algorithm (PBEDA) is proposed. Specifically, an activity-mode list is used to encode the individual of the population;a hybrid probability model is built to describe the probability distribution of the solution space;and two Pareto archives are adopted to store the explored non-dominated solutions and the solutions for updating the probability model, respectively. New individuals are generated in the promising search areas by sampling and updating the hybrid probability model. Besides, Taguchi method of design of experiments is adopted to study the effect of parameter setting. Finally, numerical results and the comparisons to other algorithms are provided to show the effectiveness of the PBEDA in terms of quantity and quality of the obtained solutions. The Pareto set derived by the PBEDA can be helpful for project manager to recognize the relationship between carbon emissions and makespan so as to properly trade-off the two criteria according to certain preference. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Low-carbon production Project scheduling estimation of distribution algorithm Probability model Multi-objective optimization

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Graphical model based continuous estimation of distribution algorithm

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APPLIED SOFT COMPUTING 2017年 58卷 388-400页

作者： PourMohammadBagher, Latifeh Ebadzadeh, Mohammad Mehdi Safabakhsh, Reza Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran

In this paper, a new estimation of distribution algorithm is introduced. The goal is to propose a method that avoids complex approximations of learning a probabilistic graphical model and considers multivariate dependencies between continuous random variables. A parallel model of some subgraphs with a smaller number of variables is learned as the probabilistic graphical model. In each generation, the joint probability distribution of the selected solutions is estimated using a Gaussian Mixture model. Then, learning the graphical model of dependencies among random variables and sampling are done separately for each Gaussian component. In the learning step, using the selected solutions of each Gaussian mixture component, the structure of a Markov network is learned. This network is decomposed to maximal cliques and a clique graph. Then, complete Bayesian network structures are learned for these subgraphs using an optimization algorithm. The proposed optimization problem is a 0-1 constrained quadratic programming which finds the best permutation of variables. Then, sampling is done from each Bayesian network of each Gaussian component. The introduced method is compared with the other network-based estimation of distribution algorithms for optimization of continuous numerical functions. (C) 2017 Published by Elsevier B.V All rights reserved.

关键词： estimation of distribution algorithm Bayesian network Markov network Continuous optimization problem

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A Two-Stage estimation of distribution algorithm With Heuristics for Energy-Aware Cloud Workflow Scheduling

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第6期16卷 4183-4197页

作者： Xie, Yi Wang, Xue-Yi Shen, Zi-Jun Sheng, Yu-Han Wu, Gong-Xing Zhejiang Gongshang Univ Sch Management & Ebusiness Hangzhou 310018 Zhejiang Peoples R China

With the enormous increase in energy usage by cloud data centers for handling various workflow applications, the energy-aware cloud workflow scheduling has become a hot issue. However, there is still a need and room for improvement in both the model for estimating workflow energy consumption and the algorithm for energy-aware cloud workflow scheduling. To fill these gaps, a new model for estimating the energy consumption of the cloud workflow execution and a novel Two-Stage estimation of distribution algorithm with heuristics (TSEDA) for energy-aware cloud workflow scheduling are proposed based on the relationships among scheduling scheme, host load and power. In particular, in the proposed TSEDA, a new probability model and its updating mechanism are presented, and a two-stage coevolution strategy with some novel heuristic methods for individual generation, decoding and improvement is designed. Extensive experiments are conducted on workflow applications with various sizes and types, and the results show that the proposed TSEDA outperforms conventional algorithms.

关键词： Workflow estimation of distribution algorithm scheduling cloud computing energy consumption

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Simulation optimization for a flexible jobshop scheduling problem using an estimation of distribution algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第1-4期73卷 3-21页

作者： Perez-Rodriguez, Ricardo Joens, S. Hernandez-Aguirre, Arturo Alberto-Ochoa, Carlos CIATEC Guanajuato 37545 Mexico UPGTO CONACYT Guanajuato 38483 Mexico CIMAT Guanajuato 36240 Mexico UACJ Ciudad Juarez 32310 Chihuahua Mexico

The flexible jobshop scheduling problem permits the operation of each job to be processed by more than one machine. The idea is to assign the processing sequence of operations on the machines and the assignment of operations on machines such that the system objectives can be optimized. The assignment mentioned is a difficult task to implement on real manufacturing environments because there are many assumptions to satisfy, especially when the amount of work is not constant or sufficient to keep the manufacturing process busy for a long time, causing intermittent idle times. An estimation of distribution algorithm-based approach coupled with a simulation model is developed to solve the problem and implement the solution. Using the proposed approach, the shop performance can be noticeably improved when different machines are assigned to different schedules.

关键词： Scheduling Event-discrete simulation estimation of distribution algorithm Simulation optimization Work in process Flexible jobshop scheduling problem

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Improved hybrid estimation of distribution algorithm for distributed parallel assembly permutation flow shop scheduling problem

IET COLLABORATIVE INTELLIGENT MANUFACTURING

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IET COLLABORATIVE INTELLIGENT MANUFACTURING 2024年第3期6卷

作者： Du, Lizhen Wang, Xintao Tang, Jiaqi Xu, Chuqiao Qin, Guanxing Wuhan Text Univ Hubei Key Lab Digital Text Equipment Wuhan Hubei Peoples R China Wuhan Text Univ Sch Mech Engn & Automat Wuhan Hubei Peoples R China

Distributed assembly permutation flow shop scheduling problem is the hot spot of distributed pipeline scheduling research;however, parallel assembly machines are often in the assembly stage. Therefore, we propose and study distributed parallel assembly permutation flow shop scheduling problem (DPAPFSP). This aims to enhance the efficiency of multi-factory collaborative production in a networked environment. Initially, a corresponding mathematical model was established. Then, an improved hybrid distribution estimation algorithm was proposed to minimize the makespan. The algorithm adopts a single-layer permutation encoding and decoding strategy based on the rule of the Earliest Finished Time. A local neighbourhood search based on critical paths is performed for the optimal solution using five types of neighborhood design. A dual sampling strategy based on repetition rates was introduced to ensure the diversity of the population in the later periods of iteration. Simulated annealing searching was applied to accelerate the decline of optimal value. Finally, we conduct simulation experiments using 900 arithmetic cases and compare the simulation experimental data of this algorithm with the other four existing algorithms. The analysis results demonstrate this improved algorithm is very effective and competitive in solving the considered DPAPFSP. The authors introduce the distributed parallel assembly permutation flow shop scheduling problem, aiming to enhance collaborative production efficiency among multiple factories in a networked environment. A mathematical model is established and an improved hybrid distribution estimation algorithm is proposed to minimise the makespan. image

关键词： distributed scheduling estimation of distribution algorithm local neighborhood search parallel assembly permutation flow shop

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