The Energy Resource Management (ERM) can be modeled as a Mixed-Integer Non-Linear Problem whose aim is to maximize profits generally using smart grid capabilities more than importing energy from external markets. Due ...
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The Energy Resource Management (ERM) can be modeled as a Mixed-Integer Non-Linear Problem whose aim is to maximize profits generally using smart grid capabilities more than importing energy from external markets. Due to this, many resources and customers are involved in optimization, making ERM a complex problem. Moreover, when the inherent uncertainty of weather conditions, load forecast, electric vehicles planned trips, or market prices is considered, deterministic approaches might fail in obtaining optimal solutions to the problem. In this context, evolutionary algorithms are a useful tool to find effective near-optimal solutions. In fact, to design and test evolutionary algorithms to solve the ERM problem under uncertainty, the research community has developed a simulation framework. In this paper, we propose the Cellular Univariate Marginal distributionalgorithm with Normal-Cauchy distribution (CUMDANCauchy) to address the ERM problem in uncertain environments. CUMDANCauchy uses a univariate estimation of the product of Normal and Cauchy distributions over each feature, and produces new individuals not only by the sampling of the learned distributions but also using neighborhoods of individuals from a ring cellular structure. The experiments performed over two case studies show that: CUMDANCauchy is as competitive as the previous dominant class of algorithms in terms of the global fitness achieved;its convergence behavior is among the best in comparison with the other tested algorithms;its running time is similar to the algorithm with the best global fitness achieved in the first case study, and it is the fastest algorithm in the second one.
The distributed assembly permutation flow-shop scheduling problem (DAPFSP) is a typical NP-hard combinatorial optimization problem that has wide applications in advanced manufacturing systems and modern supply chains....
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The distributed assembly permutation flow-shop scheduling problem (DAPFSP) is a typical NP-hard combinatorial optimization problem that has wide applications in advanced manufacturing systems and modern supply chains. In this work, an innovative three-dimensional matrix-cube-based estimation of distribution algorithm (MCEDA) is first proposed for the DAPFSP to minimize the maximum completion time. Firstly, a matrix cube is designed to learn the valuable information from elites. Secondly, a matrix-cube-based probabilistic model with an effective sampling mechanism is developed to estimate the probability distribution of superior solutions and to perform the global exploration for finding promising regions. Thirdly, a problem-dependent variable neighborhood descent method is proposed to perform the local exploitation around these promising regions, and several speedup strategies for evaluating neighboring solutions are utilized to enhance the computational efficiency. Furthermore, the influence of the parameters setting is analyzed by using design-of-experiment technique, and the suitable parameters are suggested for different scale problems. Finally, a comprehensive computational campaign against the state-of-the-art algorithms in the literature, together with statistical analyses, demonstrates that the proposed MCEDA produces better results than the existing algorithms by a significant margin. Moreover, the new best-known solutions for 214 instances are improved.
Superior task scheduling scheme is able to improve the performance in achieving shorter task completion time in multi-processor computing system. Large scale applications are generally modelled as direct acyclic graph...
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Superior task scheduling scheme is able to improve the performance in achieving shorter task completion time in multi-processor computing system. Large scale applications are generally modelled as direct acyclic graph (DAG) to be processed efficiently in parallel. To solve DAG task scheduling problem (DAG-SP) with the criterion of minimizing makespan, this paper proposes an estimation of distribution algorithm (EDA) enhanced by the path relinking. An efficient hybrid scheme integrating list scheduling heuristics is designed to take advantage of the knowledge of existing works. In addition, to describe the relative position relationships between the task pairs, a specific probability model is built and the task processing permutations are produced by sampling such a model. To enhance the exploitation of EDA, a path relinking based knowledge is used to design the local search method. Simulation experiments are carried out with both benchmark datasets and real-world graphs, where the comparative results show that the above designs can improve the performance effectively. Moreover, the numerical comparisons show that the proposed algorithm performs significantly better than the existing heuristics and evolutionary algorithms. (C) 2021 Elsevier B.V. All rights reserved.
Distributed flexible job shop scheduling has attracted research interest due to the development of global man-ufacturing. However, constraints including crane transportation and energy consumption should be considered...
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Distributed flexible job shop scheduling has attracted research interest due to the development of global man-ufacturing. However, constraints including crane transportation and energy consumption should be considered with the realistic requirements. To address this issue, first, we modeled the problem by utilizing an integer programming method, wherein the makespan and energy consumptions during the machine process and crane transportation are optimized simultaneously. Afterward, a hybrid algorithm consisting of estimation of distri-bution algorithm (EDA) and variable neighborhood search (VNS) was proposed to solve the problem, where an identification rule of four crane conditions was designed to make fitness calculation feasible. In EDA compo-nent, the parameters in probability matrices are set to be self-adaptive for stable convergence to obtain better output. Moreover, a probability mechanism was applied to control the activity of the EDA component. In VNS component, five problem-specific neighborhood structures including global and local strategies are employed to enhance exploitation ability. The simulation tests results confirmed that the proposed hybrid EDA-VNS algo-rithm can solve the considered problem with high efficiency compared with other competitive algorithms, and the proposed improving strategies are verified to have significance in better performance.
This article presents an effective estimation of distribution algorithm, named P-EDA, to solve the blocking flow-shop scheduling problem (BFSP) with the makespan criterion. In the P-EDA, a Nawaz-Enscore-Ham (NEH)-base...
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This article presents an effective estimation of distribution algorithm, named P-EDA, to solve the blocking flow-shop scheduling problem (BFSP) with the makespan criterion. In the P-EDA, a Nawaz-Enscore-Ham (NEH)-based heuristic and the random method are combined to generate the initial population. Based on several superior individuals provided by a modified linear rank selection, a probabilistic model is constructed to describe the probabilistic distribution of the promising solution space. The path relinking technique is incorporated into EDA to avoid blindness of the search and improve the convergence property. A modified referenced local search is designed to enhance the local exploitation. Moreover, a diversity-maintaining scheme is introduced into EDA to avoid deterioration of the population. Finally, the parameters of the proposed P-EDA are calibrated using a design of experiments approach. Simulation results and comparisons with some well-performing algorithms demonstrate the effectiveness of the P-EDA for solving BFSP.
An interactive evolutionary algorithm (IEA) is powerful for solving personalized search when the user's preference can be well caught, expressed, and applied in the process of searching. Hybrid recommendation by a...
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An interactive evolutionary algorithm (IEA) is powerful for solving personalized search when the user's preference can be well caught, expressed, and applied in the process of searching. Hybrid recommendation by articulating the content-based and collaborative filtering techniques is popular and effective for the personalized recommendation, but has not been developed to improve the performance of IEA for fulfilling the personalized search. Accordingly, we here propose an enhanced interactive estimation of distribution algorithm by designing dual-probabilistic models based on the hybrid recommendation for personalized search. The concept of hybrid personalized search is first defined from the viewpoint of using not only the historical search information but also the social or group preference. A dual-probabilistic model by sufficiently combining the content-based and collaborative filtering is presented and used to design the effective interactive estimation of distribution algorithm (IEDA). The probabilistic model is directly combined with the initialization of IEDA for illuminating the sparsity of the traditional IEA in encoding. The effectiveness of the proposed algorithm in fast and efficient searching with a lower computational cost is experimentally illustrated by two typical personalized searches on movies and TV series described with documents.
This article investigates how to use the estimation of distribution algorithm based on Levy flight to solve the set-union knapsack problem (SUKP). First, the mathematical model of the SUKP is introduced. Then, a quadr...
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This article investigates how to use the estimation of distribution algorithm based on Levy flight to solve the set-union knapsack problem (SUKP). First, the mathematical model of the SUKP is introduced. Then, a quadratic greedy repair and optimization algorithm (Q-GROA), which deals with infeasible solutions, is proposed. Thereby, a new approach, the estimation of distribution algorithm based on Levy flight (LFEDA) combined with the Q-GROA is also proposed to solve the SUKP. A number of experiments are performed on the SUKP datasets to evaluate the performance of our proposed model. The results verify that the proposed method is significantly better than other algorithms with respect to the solution's performance.
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...
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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.
作者：
Wang, ZhaoGong, MaoguoXidian 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 w...
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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.
It is very hard, if not impossible to use analytical objective functions for optimization of personalized search due to the difficulties in mathematically describing qualitative problems. To solve such optimization pr...
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It is very hard, if not impossible to use analytical objective functions for optimization of personalized search due to the difficulties in mathematically describing qualitative problems. To solve such optimization problems, interactive evolutionary algorithms, which can make use of human preferences, are highly desirable. However, due to the lack of effective encoding methods, interactive evolutionary algorithms have been limited to numerically encoded optimization problems. In practice, however, linguistic terms (words) are the most natural expression of human preferences, and they are also commonly used to describe items in personalized search or E-commerce;therefore, language models better suit encoding, and the optimization of personalized search is converted into a dynamic document matching problem. To optimize word-described personalized search, we propose a novel interactive estimation of distribution algorithm. This algorithm combines a language model-based encoding approach, a Dirichlet-Multinomial compound distribution-based preference expression, and a Bayesian inference mechanism. The proposed algorithm is applied to two personalized search cases to demonstrate the capability of the algorithm in ensuring a more efficient and accurate search with less user fatigue. (C) 2020 Elsevier B.V. All rights reserved.
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