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Solving system-level synthesis problem by a multi-objective estimation of distribution algorithm

EXPERT SYSTEMS WITH APPLICATIONS 2014年第5期41卷 2496-2513页

作者： Wang, Ling Fang, Chen Suganthan, Ponnuthurai Nagaratnam Liu, Min Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, the system-level synthesis problem (SLSP) is modeled as a multi-objective mode-identity resource-constrained project scheduling problem with makespan and resource investment criteria (MOMIRCPSP-MS-RI). Then, a hybrid Pareto-archived estimation of distribution algorithm (HPAEDA) is presented to solve the MOMIRCPSP-MS-RI. To be specific, the individual of the population is encoded as the activity-mode-priority-resource list (AMPRL), and a hybrid probability model is used to predict the most promising search area, and a Pareto archive is used to preserve the non-dominated solutions that have been explored, and another archive is used to preserve the solutions for updating the probability model. Moreover, specific sampling mechanism and updating mechanism for the probability model are both provided to track the most promising search area via the EDA-based evolutionary search. Finally, the modeling methodology and the HPAEDA are tested by an example of a video codec based on the H.261 image compression standard. Simulation results and comparisons demonstrate the effectiveness of the modeling methodology and the proposed algorithm. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： estimation of distribution algorithm Probability model System-level synthesis problem Project scheduling Image compression standard

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An estimation of distribution algorithm Based Dynamic Clustering Approach for Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2017年第3期97卷 4697-4727页

作者： Jiao, Dongbin Ke, Liangjun Yang, Weibo Li, Jing Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Xian Satellite Control Ctr State Key Lab Astronaut Dynam Xian 710043 Shaanxi Peoples R China

The design of energy efficiency is a very challenging issue for wireless sensor networks (WSNs). Clustering provides an effective means of tackling the issue. It could reduce energy consumption of the nodes and prolong the network lifetime. However, cluster heads deplete more energy since they bear great load of receiving, aggregation and transmission data than sensor nodes in WSNs. Therefore, the load-balanced clustering is a most significant problem for WSNs with unequal load of the sensor nodes but it is known to be an NP-hard problem. In this paper, we introduce a new model for this problem in which the objective function is to maximize the overall minimum lifetime of the cluster heads. To solve this model, we propose a novel estimation of distribution algorithm based dynamic clustering approach (EDA-MADCA). In EDA-MADCA, a new vector encoding is introduced for representing a complete clustering solution and a probability matrix model is constructed to guide the individual search. In addition, EDA-MADCA merges the EDA based exploration and the local search based exploitation within the memetic algorithm framework. A minimum-lifetime-based local search strategy is presented to avoid invalid search and enhance the local exploitation of the EDA. Experiment results demonstrate that EDA-MADCA can prolong network lifetime, it outperforms the existing DECA algorithm in terms of various performance metrics.

关键词： Energy efficiency Load-balanced clustering estimation of distribution algorithm Memetic algorithm Minimum lifetime Wireless sensor networks

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A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2012年第4期62卷 917-926页

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

In this paper, an effective bi-population based estimation of distribution algorithm (BEDA) is proposed to solve the flexible job-shop scheduling problem (FJSP) with the criterion to minimize the maximum completion time (makespan). The BEDA stresses the balance between global exploration and local exploitation. In the framework of estimation of distribution algorithm, two sub-populations are used to adjust the machine assignment and operation sequence respectively with a splitting criterion and a combination criterion. At the initialization stage, multiple strategies are utilized in a combination way to generate the initial solutions. At the global exploration phase, a probability model is built with the superior population to generate the new individuals and a mechanism is proposed to update the probability model. At the local exploitation phase, different operators are well designed for the two sub-populations to generate neighbor individuals and a local search strategy based on critical path is proposed to enhance the exploitation ability. In addition, the influence of parameters is investigated based on Taguchi method of design of experiment, and a suitable parameter setting is determined. Finally, numerical simulation based on some widely used benchmark instances is carried out. The comparisons between BEDA and some existing algorithms as well as the single-population based EDA demonstrate the effectiveness of the proposed BEDA in solving the FJSP. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Flexible job-shop scheduling problem estimation of distribution algorithm Bi-population Probability model Critical path Design of experiment

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A hybrid estimation of distribution algorithm for the semiconductor final testing scheduling problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2015年第5期26卷 861-871页

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

As the last process of the semiconductor fabrication, the final testing is crucial to guarantee the quality of the integrated circuit products. The semiconductor final testing scheduling problem (SFTSP) is of great significance to the efficiency of the semiconductor companies. To find satisfactory solutions within reasonable computational time, the intelligent manufacturing scheduling based on the meta-heuristic methods has become a common approach. In this paper, a hybrid estimation of distribution algorithm (HEDA) is proposed to solve the SFTSP. First, novel encoding and decoding methods are proposed to map from the solution space to the schedule space effectively. Second, a probability model that describes the distribution of the solution space is built to generate the new individuals of the population. Third, a mechanism is used to update the parameters of the probability model with the superior solutions at every generation. Furthermore, to enhance the exploitation ability of the algorithm, a local search procedure is hybridized to find neighbor solutions of the promising individuals obtained by sampling the probability model. In addition, the influence of parameters is investigated based on Taguchi method of design-of-experiment, and a set of suitable parameters is suggested. Finally, numerical simulation based on some benchmark instances is carried out. The comparisons between the HEDA and some existing algorithms demonstrate the effectiveness of the proposed HEDA in solving the SFTSP.

关键词： Semiconductor final testing scheduling problem estimation of distribution algorithm Probability model Local search Design-of-experiment

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Hybrid estimation of distribution algorithm for the Quay Crane Scheduling Problem

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APPLIED SOFT COMPUTING 2013年第10期13卷 4063-4076页

作者： Exposito-Izquierdo, Christopher Luis Gonzalez-Velarde, Jose Melian-Batista, Belen Marcos Moreno-Vega, J. Univ La Laguna ETS Ingn Informat Dpto Estadist IO & Computac E-38207 San Cristobal la Laguna Spain

The competitiveness of a container terminal is highly conditioned by the time that container vessels spend on it. The proper scheduling of the quay cranes can reduce this time and allows a container terminal to be more attractive to shipping companies. The goal of the Quay Crane Scheduling Problem (QCSP) is to minimize the handling time of the available quay cranes when performing the tasks of loading and unloading containers onto/from a container vessel. This paper proposes a hybrid estimation of distribution algorithm with local search to solve the QCSP. This approach includes a priori knowledge about the problem in the initialization step to reach promising regions of the search space as well as a novel restarting strategy with the aim of avoiding the premature convergence of the search. Furthermore, an approximate evaluation scheme is applied in order to reduce the computational burden. Moreover, its performance is statistically compared with the best optimization method from the literature. Numerical testing results demonstrate the high robustness and efficiency of the developed technique. Additionally, some relevant components of the scheme are individually analyzed to check their effectiveness. (C) 2013 Elsevier B. V. All rights reserved.

关键词： Quay Crane Scheduling Problem Container terminal estimation of distribution algorithm Local search Heuristics

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An estimation of distribution algorithm for scheduling problem of flexible manufacturing systems using Petri nets

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APPLIED MATHEMATICAL MODELLING 2018年 55卷 776-788页

作者： Wang, XinNian Xing, KeYi Li, XiaoLing Luo, JianChao Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Syst Engn Inst Xian 710049 Peoples R China

Based on the place-timed Petri net models of flexible manufacturing systems (FMSs), this paper proposes a novel effective estimation of distribution algorithm (EDA) for solving the scheduling problem of FMSs. A candidate solution is represented as an individual with two sections: the first contains the route information while the second is a permutation with repetition for parts. The feasibility of individuals is checked and guaranteed by a highly permissiveness deadlock controller. A feasible individual is interpreted into a deadlock-free schedule while the infeasible ones are amended. The probabilistic model in EDA is constructed via a voting procedure. An offspring individual is then produced based on the model from a seed individual, and the set of seed individuals is extracted by a roulette method from the current population. The longest common subsequence is also embedded into the probabilistic model for mining good genes. A modified variable neighborhood search is applied on offspring individuals to obtain better solutions in their neighbors and hence to improve EDA's performance. Computational results show that our proposed algorithm outperforms all the existing ones on benchmark examples for the studied problem. It is of important practice significance for the manufacturing of time-critical and multi-type products. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Flexible manufacturing system Timed Petri net Scheduling estimation of distribution algorithm Variable neighborhood search

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Approximating the Set of Pareto-Optimal Solutions in Both the Decision and Objective Spaces by an estimation of distribution algorithm

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2009年第5期13卷 1167-1189页

作者： Zhou, Aimin Zhang, Qingfu Jin, Yaochu E China Normal Univ Dept Comp Sci & Technol Shanghai 200241 Peoples R China Univ Essex Sch Elect Engn & Comp Sci Colchester CO4 3SQ Essex England Honda Res Inst Europe D-63073 Offenbach Germany

Most existing multiobjective evolutionary algorithms aim at approximating the Pareto front (PF), which is the distribution of the Pareto-optimal solutions in the objective space. In many real-life applications, however, a good approximation to the Pareto set (PS), which is the distribution of the Pareto-optimal solutions in the decision space, is also required by a decision maker. This paper considers a class of multiobjective optimization problems (MOPs), in which the dimensionalities of the PS and the PF manifolds are different so that a good approximation to the PF might not approximate the PS very well. It proposes a probabilistic model-based multiobjective evolutionary algorithm, called MMEA, for approximating the PS and the PF simultaneously for an MOP in this class. In the modeling phase of MMEA, the population is clustered into a number of subpopulations based on their distribution in the objective space, the principal component analysis technique is used to estimate the dimensionality of the PS manifold in each subpopulation, and then a probabilistic model is built for modeling the distribution of the Pareto-optimal solutions in the decision space. Such a modeling procedure could promote the population diversity in both the decision and objective spaces. MMEA is compared with three other methods, KP1, Omni-Optimizer and RM-MEDA, on a set of test instances, five of which are proposed in this paper. The experimental results clearly suggest that, overall, MMEA performs significantly better than the three compared algorithms in approximating both the PS and the PF.

关键词： estimation of distribution algorithm multiobjective optimization Pareto optimality principal component analysis

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Optimal model parameter estimation of solar and fuel cells using improved estimation of distribution algorithm

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AIN SHAMS ENGINEERING JOURNAL 2021年第2期12卷 1693-1700页

作者： Chandran, Benin Pratap Selvakumar, A. Immanuel Let, G. Shine Sathiyan, S. Paul Karunya Inst Technol & Sci Dept EEE Coimbatore 641114 Tamil Nadu India Karunya Inst Technol & Sci Dept ECE Coimbatore 641114 Tamil Nadu India

Renewable energy through the use of fuel cells and solar cells is one of the popular developments in recent days that produce electricity. Accurate modelling of fuel cell and solar cells are essential in simulation and analysis of energy systems with these sources. However, the systems are extremely nonlinear and complicated. The model needs to be optimized under distinct operating circumstances. Enhanced and streamlined Improved estimation of distribution (IED) algorithm is suggested in this paper to estimate the parameter through optimization for solar cell models and fuel cell models. This is accomplished through the introduction of an ideal approach to improve population quality and the use of a local search to improve the efficiency of the finest global solution further. The design of an IED algorithm is much more straightforward and search efficiency is greatly improved compared with the fundamental optimization techniques from the literature. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： Solar cell Fuel cell estimation of distribution algorithm Optimization Parameter estimation

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An estimation of distribution algorithm for minimizing the total flowtime in permutation flowshop scheduling problems

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COMPUTERS & OPERATIONS RESEARCH 2009年第9期36卷 2638-2646页

作者： Jarboui, Bassem Eddaly, Mansour Siarry, Patrick Univ Paris 12 LiSSi F-94010 Creteil France FSEGS Sfax 3018 Tunisia

In this work we propose an estimation of distribution algorithm (EDA) as a new tool aiming at minimizing the total flowtime in permutation flowshop scheduling problems. A variable neighbourhood search is added to the algorithm as an improvement procedure after creating a new offspring. The experiments show that our approach outperforms all existing techniques employed for the problem and can provide new upper bounds. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： estimation of distribution algorithm Variable neighbourhood search Scheduling Permutation flowshop Flowtime

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Handling Uncertainty in Financial Decision Making: A Clustering estimation of distribution algorithm With Simplified Simulation

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021年第1期5卷 42-56页

作者： Shi, Wen Chen, Wei-Neng Gu, Tianlong Jin, Hu Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Hanyang Univ Div Elect Engn Ansan 15588 South Korea

In financial decision making models, parameters are usually obtained based on historical data, which involve strong uncertainties. In some cases, the fluctuation caused by environmental uncertainty may even be more significant than that caused by utilizing different strategies. Such phenomenon makes the optimization and uncertainty handling in finical optimization a great challenge. In this article, a group insurance portfolio problem is considered as an instance of financial optimization with strong uncertainty. To handle uncertainty, we first analyze the feature of the problem and discover that in such kind of optimization problem with strong uncertainty, the solutions are strongly relative to the scenario. In view of the scenario-relevant feature, a simplified simulation approach is designed. Only one scenario is simulated for each generation in the evolution process to deal with the uncertainties. Combining this approach with a clustering estimation of distribution algorithm, a new algorithm (CEDA-SS) is proposed. estimation of current profit is made by Monte Carlo (MC) simulation based on historical data. Solutions in each generation are evaluated in the same scenario. Two kinds of clustering mechanisms are applied to further improve the performance of the algorithm. Moreover, a comparison mechanism based on the Wilcoxon rank sum test is proposed to evaluate the performance of the algorithms. Experimental results show that the proposed CEDA-SS is suitable for the group insurance portfolio problem and it outperforms other uncertain evolutionary algorithms.

关键词： Uncertainty Decision making Optimization Insurance Portfolios Clustering algorithms Computational modeling Financial decision making optimization under uncertainty insurance portfolio estimation of distribution algorithm

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