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A copula-based hybrid estimation of distribution algorithm for m-machine reentrant permutation flow-shop scheduling problem

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APPLIED SOFT COMPUTING 2017年 61卷 921-934页

作者： Qian, Bin Li, Zuo-cheng Hu, Rong Kunming Univ Sci & Technol Fac Informat Engn & Automat Dept Automat Kunming 650500 Yunnan Peoples R China

Aiming at the m-machine reentrant permutation flow-shop scheduling problem (MRPFSSP), a copula based hybrid estimation of distribution algorithm (CHEDA) is presented to minimize the makespan criterion. Firstly, we establish both the operation-based model and the graph model for MRPFSSP, and then several inherent properties about critical path and blocks are proposed and analyzed. Secondly, the copula theory is utilized to build CHEDA's probability model (i.e., the joint distribution function, JDF) to efficiently extract the useful information from the excellent individuals. Thirdly, the global search based on the JDF model and a new population sampling method is designed to find the promising sub-regions in the total solution space. Fourthly, a problem-dependent local search based on the critical path and blocks is embedded into CHEDA to enhance the local exploitation ability. Finally, simulation experiments and comparisons demonstrate the effectiveness of the proposed CHEDA. (C) 2017 Published by Elsevier B.V.

关键词： estimation of distribution algorithm Copula theory m-Machine reentrant permutation flow-shop Critical path Local search

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An estimation of distribution algorithm and new computational results for the stochastic resource-constrained project scheduling problem

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2015年第4期27卷 585-605页

作者： Fang, Chen Kolisch, Rainer Wang, Ling Mu, Chundi Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China Tech Univ Munich TUM Sch Management D-80333 Munich Germany

In this paper we propose an estimation of distribution algorithm (EDA) to solve the stochastic resource-constrained project scheduling problem. The algorithm employs a novel probability model as well as a permutation-based local search. In a comprehensive computational study, we scrutinize the performance of EDA on a set of widely used benchmark instances. Thereby, we analyze the impact of different problem parameters as well as the variance of activity durations. By benchmarking EDA with state-of-the-art algorithms, we can show that its performance compares very favorably to the latter, with a clear dominance in instances with medium to high variance of activity duration.

关键词： Stochastic resource-constrained project scheduling estimation of distribution algorithm Permutation-based local search Impact of problem parameters

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Multi-Objective Optimization with estimation of distribution algorithm in a Noisy Environment

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EVOLUTIONARY COMPUTATION 2013年第1期21卷 149-177页

作者： Shim, Vui Ann Tan, Kay Chen Chia, Jun Yong Al Mamun, Abdullah Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs;particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms.

关键词： estimation of distribution algorithm evolutionary algorithm multi-objective optimization noisy fitness function particle swarm optimization restricted Boltzmann machine

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Gesture segmentation based on a two-phase estimation of distribution algorithm

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INFORMATION SCIENCES 2017年 394卷 88-105页

作者： Liu, Ke Gong, Dunwei Meng, Fanlin Chen, Huanhuan Wang, Gai-Ge China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Peoples R China LanZhou Univ Technol Sch Elect Engn & Informat Engn Lanzhou 730050 Peoples R China Univ Durham Sch Engn & Comp Sci Durham DH1 3LE England Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Peoples R China

A multi-objective optimization model for the problem of gesture segmentation is formulated, and a method of solving the model based on a two-phase estimation of distribution algorithm is presented. When building the model, the positions of a series of pixels are taken as the decision variable, and the differences between the colors of pixels and those of a hand are taken as objective functions. A method of gesture segmentation based on a two-phase estimation of distribution algorithm is proposed according to the correlation among the positions of pixels. The method divides the solution of the problem based on evolutionary optimization into two phases, and uses different estimation of distribution algorithms in different phases. In the first phase, the probability model of candidates is formulated by a number of intervals given the fact that the positions of hand pixels distribute in several intervals. In the second phase, the probability model of candidates is built through a series of segments since the positions of hand pixels further distribute around curves. A series of pixels constituting a hand region are obtained based on sampling by the above probability models. The proposed method is applied to 2515 problems of gesture segmentation, and is compared with the existing methods. The experimental results demonstrate the effectiveness of the proposed method. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Gesture segmentation estimation of distribution algorithm Two-phase Probability model Sampling

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Minimizing the resource consumption of heterogeneous batch-processing machines using a copula-based estimation of distribution algorithm

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APPLIED SOFT COMPUTING 2018年 73卷 283-305页

作者： Liu, Chuang Chen, Huaping Xu, Rui Wang, Yu Univ Sci & Technol China Sch Management Hefei 230026 Anhui Peoples R China Hohai Univ Sch Business Nanjing 210098 Jiangsu Peoples R China

The two-stage flow-shop scheduling problem investigated in this work aims minimize the resource consumption of non-identical job sizes. The flow shop consists of two batch-processing machines (BPMs): a parallel batch BPM and a sequential BPM. The makespan and resource consumption are considered together in this study, the makespan is the constraint condition, and the resource consumption is the objective. A copula-based estimation of distribution algorithm (cEDA) is used to solve the problem. In this study, the individuals are coded by the allocated resource sequences of all jobs in two machines, and the convex resource consumption function is adopted to simulate the relationship between the processing time of the jobs and the resources allocated to the jobs. A Gaussian distribution is adopted as the marginal probabilistic distribution of all the components. The proposed copula function C-1 assumes independence among the components, whereas the Clayton copula function C-2 assumes that all components are interrelated and introduced for comparison. The computational experiments and comparisons verify the effectiveness of the proposed cEDA. In addition, the copula functions C-1 and C-2 adopted in the proposed cEDA approach are compared. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Flow shop Batch-processing machines estimation of distribution algorithm Resource consumption

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Hybrid estimation of distribution algorithm for solving Single Row Facility Layout Problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2013年第1期66卷 95-103页

作者： Ou-Yang, Chao Utanilma, Amalia Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Inst Teknol Sepuluh Nopember Dept Informat Syst Surabaya Indonesia

The layout positioning problem of facilities on a straight line is known as Single Row Facility Layout Problem (SRFLP). The objective of SRFLP, categorized as NP Complete problem, is to arrange the layout so that the sum of distances between all facilities' pairs can be minimized. estimation of distribution algorithm (EDA) efficiently improves the solution quality in first few runs, but the diversity loss grows rapidly as more iterations are run. To maintain the diversity, hybridization with metaheuristic algorithms is needed. This research proposes Hybrid estimation of distribution algorithm (EDAhybrid), an algorithm which consists of hybridization of EDA, Particle Swarm Optimization (PSO), and Tabu Search. Another hybridization algorithm, extended Artificial Chromosomes Genetic algorithm (eACGA), is also built as benchmark. EDAhybrid's performance is tested in 15 benchmark problems of SRFLP and it successfully achieves optimum solution. Moreover, the mean error rates of EDAhybrid always get the lowest value compared to other algorithms. SRFLP can be enhanced by considering more constraints, so it becomes enhanced SRFLP. Computational results show that EDAhybrid can also solve Enhanced SRFLP effectively. Therefore, we can conclude that EDAhybrid is a promising metaheuristic algorithm which can be used to solve the basic and enhanced SRFLP. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Single row facility layout estimation of distribution algorithm Particle Swarm Optimization Tabu Search

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A Pareto-based estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2013年第12期51卷 3574-3592页

作者： Wang, Ling Wang, Shengyao Liu, Min Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

To solve the multi-objective flexible job-shop problem (MFJSP), an effective Pareto-based estimation of distribution algorithm (P-EDA) is proposed. The fitness evaluation based on Pareto optimality is employed and a probability model is built with the Pareto superior individuals for estimating the probability distribution of the solution space. In addition, a mechanism to update the probability model is proposed, and the new individuals are generated by sampling the promising searching region based on the probability model. To avoid premature convergence and enhance local exploitation, the population is divided into two sub-populations at certain generations according to a splitting criterion, and different operators are designed for the two sub-populations to generate the promising neighbour individuals. Moreover, multiple strategies are utilised in a combination way to generate the initial solutions, and a local search strategy based on critical path is proposed to enhance the exploitation ability. Furthermore, the influence of parameters is investigated based on the Taguchi method of design of experiment, and a suitable parameter setting is suggested. Finally, numerical simulation based on some well-known benchmark instances and comparisons with some existing algorithms are carried out. The comparative results demonstrate the effectiveness of the proposed P-EDA in solving the MFJSP.

关键词： multi-objective flexible job-shop scheduling problem estimation of distribution algorithm Pareto optimality probability model critical path design of experiment

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Multi-Objective Deep Network-Based estimation of distribution algorithm for Music Composition

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IEEE ACCESS 2022年 10卷 71973-71985页

作者： Jeong, Jae-Hun Lee, Eunbin Lee, Jong-Hyun Ahn, Chang Wook CreativeMind Inc Res & Dev Ctr Suwon 16512 South Korea Gwangju Inst Sci & Technol GIST Sch Artificial Intelligence Gwangju 61005 South Korea Sungkyunkwan Univ Res Ctr Convergence Suwon 16419 South Korea

In the field of evolutionary algorithm music composition, most of the current researches focus on how to enhance environmental selection based on multi-objective evolutionary algorithms (MOEAs). However, the real music composition process defined as large-scale multi-optimization problems (LSMOP) involve the number of combinations, and the existing MOEA-based optimization process can be challenging to effectively explore the search space. To address this issue, we propose a new Multi-Objective Generative Deep network-based estimation of distribution algorithm (MODEDA) based on dimensionality reduction in decision space. In order to alleviate the difficulties with dimensional transformation, we propose a novel solution search method that optimizes in the transformed space and ensures consistency between the pareto sets of the original problem. The proposed algorithm is tested on the knapsack problems and music composition experiments. The experimental results have demonstrated that the proposed algorithm has excellency in terms of its optimization performance and computational efficiency in LSMOP.

关键词： Optimization estimation Search problems Statistics Space exploration Sociology Music Evolutionary music composition multi-objective optimization generative deep networks estimation of distribution algorithm artificial intelligent music composition

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A hybrid estimation of distribution algorithm for solving the resource-constrained project scheduling problem

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第3期39卷 2451-2460页

作者： Wang, Ling Fang, Chen Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, a hybrid estimation of distribution algorithm (HEDA) is proposed to solve the resource-constrained project scheduling problem (RCPSP). In the HEDA, the individuals are encoded based on the extended active list (EAL) and decoded by serial schedule generation scheme (SGS), and a novel probability model updating mechanism is proposed for well sampling the promising searching region. To further improve the searching quality, a Forward-Backward iteration (FBI) and a permutation based local search method (PBLS) are incorporated into the EDA based search to enhance the exploitation ability. Simulation results based on benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed HEDA. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Resource-constrained project scheduling estimation of distribution algorithm Probability model Permutation based local search Hybrid algorithm

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Stochastic resource-constrained project scheduling problem with time varying weather conditions and an improved estimation of distribution algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 157卷 107322-107322页

作者： Zhou, Yifan Miao, Jindan Yan, Bin Zhang, Zhisheng Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China Beijing Goldwind Sci & Creat Windpower Equipment Beijing 100176 Peoples R China

Construction projects with outdoor operations are affected by time-varying weather conditions. However, most existing research on stochastic resource-constrained project scheduling problems (SRCPSPs) considers activity duration as a random variable from a time-independent distribution. To address this issue, this study investigates SRCPSP under time-varying weather conditions;an improved estimation of distribution algorithm (EDA) including a ranking and selection method using common random numbers is proposed for enhancing the performance of project scheduling. The benchmark J120 dataset from PSPLIB and a practical case of windfarm construction are used to validate the improved EDA. For three randomly selected cases from the J120 dataset, the improved EDA can reduce the expected makespan by 17.0, 29.4, and 12.5 days when compared with deterministic scheduling. The corresponding makespan reductions obtained by the original EDA are 10.8, 22.7, and 7.1 days. Similarly, the improved EDA obtains 23% higher expected makespan reduction for the practical case.

关键词： SRCPSP estimation of distribution algorithm Ranking and selection Common random numbers

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