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An effective estimation of distribution algorithm for the multi-mode resource-constrained project scheduling problem

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COMPUTERS & OPERATIONS RESEARCH 2012年第2期39卷 449-460页

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

In this paper, an estimation of distribution algorithm (EDA) is proposed to solve the multi-mode resource-constrained project scheduling problem (MRCPSP). In the EDA, the individuals are encoded based on the activity-mode list (AML) and decoded by the multi-mode serial schedule generation scheme (MSSGS), and a novel probability model and an updating mechanism are proposed for well sampling the promising searching region. To further improve the searching quality, a multi-mode forward backward iteration (MFBI) and a multi-mode permutation based local search method (MPBLS) are proposed and incorporated into the EDA based search framework to enhance the exploitation ability. Based on the design-of-experiment (DOE) test, suitable parameter combinations are determined and some guidelines are provided to set the parameters. Simulation results based on a set of benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed EDA. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Multi-mode resource-constrained project scheduling estimation of distribution algorithm Probability model Permutation based local search

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RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2008年第1期12卷 41-63页

作者： Zhang, Qingfu Zhou, Aimin Jin, Yaochu Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England Honda Res Inst Europe D-63073 Offenbach Germany

Under mild conditions, it can be induced from the Karush-Kuhn-Tucker condition that the Pareto set, in the decision space, of a continuous multiobjective optimization problem is a piecewise continuous (m - 1)-D manifold, where m Is the number of objectives. Based on this regularity property, we propose a regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) for continuous multiobjective optimization problems with variable linkages. At each generation, the proposed algorithm models a promising area in the decision space by a probability distribution whose centroid is a (m - 1) -D piecewise continuous manifold. The local principal component analysis algorithm is used for building such a model. New trial solutions are sampled from the model thus built. A nondominated sorting-based selection is used for choosing solutions for the next generation. Systematic experiments have shown that, overall, RM-MEDA outperforms three other state-of-the-art algorithms, namely, GDE3, PCX-NSGA-II, and MIDEA, on a set of test instances with variable linkages. We have demonstrated that, compared with GDE3, RM-MEDA is not sensitive to algorithmic parameters, and has good scalability to the number of decision variables in the case of nonlinear variable linkages. A few shortcomings of RM-MEDA have also been identified and discussed in this paper.

关键词： estimation of distribution algorithm local principal component analysis multiobjective optimization regularity scalability sensitivity the Karush-Kuhn-Tucker condition variable linkages

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An estimation of distribution algorithm with multiple intensification strategies for two-stage hybrid flow-shop scheduling problem with sequence-dependent setup time

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APPLIED INTELLIGENCE 2023年第5期53卷 5160-5178页

作者： Liu, Huan Zhao, Fuqing Wang, Ling Cao, Jie Tang, Jianxin Jonrinaldi Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou Peoples R China Gansu Agr Univ Coll Informat Sci & Technol Lanzhou 730070 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China Univ Andalas Dept Ind Engn Padang 25163 Indonesia

The estimation of distribution algorithm (EDA) has recently emerged as a promising alternative to the traditional evolutionary algorithms for solving combinatorial optimization problems. In this paper, an estimation of distribution algorithm with multiple intensification strategies (EDA-MIS) is proposed to solve a typical kind of hybrid flow-shop scheduling problem. The two-stage heterogeneous hybrid flow-shop scheduling problem is investigated. The sequence-dependent setup time at the first stage is also considered. In the proposed EDA-MIS, the initial population is constructed through the heuristic method and random strategy. An order matrix is established to estimate the probabilistic model of promising solutions. Then the solutions of the algorithm are evolved through the processes of selection, recombination, sampling, and local search. The obtained results indicate that the EDA-MIS provides good solutions in the aspects of solution quality and computational efficiency.

关键词： Hybrid flow-shop scheduling problem Sequence-dependent setup time estimation of distribution algorithm Local search

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A self-adaptive estimation of distribution algorithm with differential evolution strategy for supermarket location problem

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NEURAL COMPUTING & APPLICATIONS 2020年第10期32卷 5791-5804页

作者： Zhou, Bing-Hai Tan, Fen Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

In modern production systems, an ever-rising product variety has imposed great challenges for in-plant part supply systems used to feed mixed-model assembly lines with required parts. In recent years, many automotive manufacturers have identified the supermarket concept as an efficient part feeding strategy to enable JIT (Just-in-time) deliveries at low costs. This paper studies a discrete supermarket location problem which considers the utilization rate and capacity constraint of the supermarkets simultaneously. Firstly, a mathematical model is developed with the objective of minimizing the total system cost consisting of operating cost and transportation cost. Then, a self-adaptive estimation of distribution algorithm with differential evolution strategy, named DE/AEDA, is proposed to solve the problem. Finally, computational experiments are carried out to analyze the performance of the proposed algorithm compared with the benchmark algorithm by using a non-parametric test method. The results indicate that the proposed algorithm is valid and efficient.

关键词： In-plant material delivery Supermarket Location estimation of distribution algorithm

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An effective estimation of distribution algorithm for parallel litho machine scheduling with reticle constraints

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High Technology Letters 2016年第1期22卷 47-54页

作者：周炳海 Zhong Zhenyi School of Mechanical Engineering Tongji University

In order to improve the scheduling efficiency of photolithography,bottleneck process of wafer fabrications in the semiconductor industry,an effective estimation of distribution algorithm is proposed for scheduling problems of parallel litho machines with reticle constraints,where multiple reticles are available for each reticle ***,the scheduling problem domain of parallel litho machines is described with reticle constraints and mathematical programming formulations are put forward with the objective of minimizing total weighted completion ***,estimation of distribution algorithm is developed with a decoding scheme specially designed to deal with the reticle ***,an insert-based local search with the first move strategy is introduced to enhance the local exploitation ability of the ***,simulation experiments and analysis demonstrate the effectiveness of the proposed algorithm.

关键词： semiconductor manufacturing parallel machine scheduling auxiliary resource constraints estimation of distribution algorithm

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An estimation of distribution algorithm with branch-and-bound based knowledge for robotic assembly line balancing

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COMPLEX & INTELLIGENT SYSTEMS 2021年第3期7卷 1125-1138页

作者： Sun, Bin-qi Wang, Ling Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Robotic assembly lines are widely used in manufacturing industries. The robotic assembly line balancing (RALB) problem aims to balance the workloads among different workstations and optimize the assembly line efficiency. This paper addresses a particular type of RALB problem, which minimizes the assembly line cycle time by determining the task and robot assignment in each workstation under precedence constraints. To solve the problem, we present an effective hybrid algorithm fusing the estimation of distribution algorithm and branch-and-bound (B&B) based knowledge. A problem-specific probability model is designed to describe the probabilities of each task being assigned to different workstations. Based on the probability model, an incremental learning method is developed and a sampling mechanism with B&B based knowledge is proposed to generate new feasible solutions. The fuse of B&B based knowledge is able to reduce the search space of EDA while focusing the search on the promising area. To enhance the exploitation ability, a problem-specific local search is developed based on the critical workstation to further improve the quality of elite solutions. The computational complexity of the proposed algorithm is analyzed, and the effectiveness of the B&B based knowledge and the problem-specific local search is demonstrated through numerical experiments. Moreover, the performance of the proposed algorithm is compared with existing algorithms on a set of widely-used benchmark instances. Comparative results demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： estimation of distribution algorithm Sampling mechanism Branch-and-bound based knowledge Problem-specific local search Robotic assembly line balancing

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Multiobjective decomposition-based Mallows Models estimation of distribution algorithm. A case of study for permutation flowshop scheduling problem

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INFORMATION SCIENCES 2017年 397卷 137-154页

作者： Zangari, Murilo Mendiburu, Alexander Santana, Roberto Pozo, Aurora Univ Fed Parana UFPR Comp Sci Dept PO 19081 BR-81531970 Curitiba Parana Brazil Univ Basque Country UPV EHU Dept Comp Architecture & Technol Intelligent Syst Grp Paseo Manuel Lardizabal 1 San Sebastian 20080 Guipuzcoa Spain Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp Paseo Manuel Lardizabal 1 San Sebastian 20080 Guipuzcoa Spain

estimation of distribution algorithms (EDAs) have become a reliable alternative to solve a broad range of single and multi-objective optimization problems. Recently, distance-based exponential models, such as Mallows Model (MM) and Generalized Mallows Model (GMM), have demonstrated their validity in the context of EDAs to deal with permutation-based optimization problems. The aim of this paper is two-fold. First, we introduce a novel general multi-objective decomposition-based EDA using Kernels of Mallows models (MEDA/D-MK framework) for solving multi-objective permutation-based optimization problems. Second, in order to demonstrate the validity of the MEDA/D-MK, we have applied it to solve the multi-objective permutation flowshop scheduling problem (MoPFSP) minimizing the total flow time and the makespan. The permutation flowshop scheduling problem is one of the most studied problems of this kind due to its fields of application and algorithmic challenge. The results of our experiments show that MEDA/D-MK outperforms an improved MOEA/D variant specific tailored for minimizing makespan and total flowtime. Furthermore, our approach achieves competitive results compared to the best-known approximated Pareto fronts reported in the literature for the benchmark considered. (C) 2017 Elsevier Inc. All rights reserved.

关键词： estimation of distribution algorithm Mallows models Multi-objective optimization Decomposition-based Permutation optimization problems Flowshop scheduling problem

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Improved estimation of distribution algorithm for the problem of single-machine scheduling with deteriorating jobs and different due dates

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COMPUTATIONAL & APPLIED MATHEMATICS 2014年第3期33卷 557-573页

作者： Wu, Hua-Pin Huang, Min Northeastern Univ Coll Informat Sci & Engn State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

This paper investigates single-machine scheduling problem, which is an NP-hard problem, with deteriorating jobs and different due dates tominimize total tardiness. First, two special polynomially solvable cases of the problem and a mixed-integer programming (MIP) model are proposed. Since the large-scale problem needs a long time when the MIP is solved using the CPLEX, the improved estimation of distribution algorithm (EDA) is proposed to solve the problem with a large size. EDA depends on the probabilistic model, which denotes the distribution of decision variables in the feasible region space. Meanwhile, EDA owns efficient search capability and convergence. To obtain an improved initial population, an efficient initialization scheme based on the feature of two special cases and a heuristic algorithm are adopted in the process of constructing the initial population. The probabilistic model is composited based on elite solutions from each generation. Simultaneously, mutation is embedded tomaintain the diversity of the population. Compared with the results, numerical experiments show that the proposed algorithm can obtain good near-optimal solutions within a short period.

关键词： Single machine Deteriorating jobs Tardiness estimation of distribution algorithm Mixed integer programming model

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A novel ensemble estimation of distribution algorithm with distribution modification strategies

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COMPLEX & INTELLIGENT SYSTEMS 2023年第5期9卷 5377-5416页

作者： Wang, Xiaofei Li, Yintong Liang, Yajun Wu, Bi Xuan, Yongbo Beijing Blue Sky Innovat Ctr Frontier Sci Beijing 100085 Peoples R China Air Force Engn Univ Aviat Engn Sch Xian 710038 Peoples R China China Aerodynam Res & Dev Ctr Mianyang 621000 Sichuan Peoples R China

The canonical estimation of distribution algorithm (EDA) easily falls into a local optimum with an ill-shaped population distribution, which leads to weak convergence performance and less stability when solving global optimization problems. To overcome this defect, we explore a novel EDA variant with an ensemble of three distribution modification strategies, i.e., archive-based population updating (APU), multileader-based search diversification (MSD), and the triggered distribution shrinkage (TDS) strategy, named E-3-EDA. The APU strategy utilizes historical population information to rebuild the search scope and avoid ill-shaped distributions. Moreover, it continuously updates the archive to avoid overfitting the distribution model. The MSD makes full use of the location differences among populations to evolve the sampling toward promising regions. TDS is triggered when the search stagnates, shrinking the distribution scope to achieve local exploitation. Additionally, the E-3-EDA performance is evaluated using the CEC 2014 and CEC 2018 test suites on 10-dimensional, 30-dimensional, 50-dimensional and 100-dimensional problems. Moreover, several prominent EDA variants and other top methods from CEC competitions are comprehensively compared with the proposed method. The competitive performance of E-3-EDA in solving complex problems is supported by the nonparametric test results.

关键词： estimation of distribution algorithm CEC 2014 CEC 2018 Numerical optimization

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An estimation of distribution algorithm with clustering for scenario-based robust financial optimization

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COMPLEX & INTELLIGENT SYSTEMS 2022年第5期8卷 3989-4003页

作者： Shi, Wen Hu, Xiao-Min Chen, Wei-Neng South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Univ Technol Sch Comp Guangzhou Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China

One important problem in financial optimization is to search for robust investment plans that can maximize return while minimizing risk. The market environment, namely the scenario of the problem in optimization, always affects the return and risk of an investment plan. Those financial optimization problems that the performance of the investment plans largely depends on the scenarios are defined as scenario-based optimization problems. This kind of uncertainty is called scenario-based uncertainty. The consideration of scenario-based uncertainty in multi-objective optimization problem is a largely under explored domain. In this paper, a nondominated sorting estimation of distribution algorithm with clustering (NSEDA-C) is proposed to deal with scenario-based robust financial problems. A robust group insurance portfolio problem is taken as an instance to study the features of scenario-based robust financial problems. A simplified simulation method is applied to measure the return while an estimation model is devised to measure the risk. Applications of the NSEDA-C on the group insurance portfolio problem for real-world insurance products have validated the effectiveness of the proposed algorithm.

关键词： Multi-objective optimization Uncertainty handling estimation of distribution algorithm Financial investment

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