In view of the stochastic nature of the data in real-world manufacturing systems, it is crucial to develop effective algorithms to solve the scheduling problems with uncertainty. In this paper, an order-based estimati...
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In view of the stochastic nature of the data in real-world manufacturing systems, it is crucial to develop effective algorithms to solve the scheduling problems with uncertainty. In this paper, an order-based estimation of distribution algorithm (OEDA) is proposed to solve the hybrid flow-shop scheduling problem (HFSP) with stochastic processing times. Considering the effectiveness and robustness of a schedule, it aims to minimise the makespan of the initial scenario as well as the deviation of all results of the stochastic scenarios and the initial one. To be specific, a bi-objective function is used to evaluate the individuals of the population, and a probability model is designed to describe the probability distribution of the solution space. Meanwhile, optimal computing budget allocation (OCBA) technique is employed to provide a reliable identification to the good solutions among the population. A mechanism is also presented to update the probability model with the superior individuals that are identified by the OCBA. The new individuals are generated by sampling the probability model to track the area with promising solutions. In addition, the influence of parameter setting is investigated based on Taguchi method of design-of-experiment (DOE), and a suitable parameter setting is suggested. Extensive numerical testing results and comparisons with the existing algorithm are provided, which demonstrate the effectiveness of the proposed OEDA.
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 simultaneous...
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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.
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-...
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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.
This paper introduces an estimation of distribution algorithm (EDA), in which the parameters of the search distribution are updated by the natural gradient technique. The parameter updating is guided via the Kullback-...
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ISBN:
(纸本)9781450334723
This paper introduces an estimation of distribution algorithm (EDA), in which the parameters of the search distribution are updated by the natural gradient technique. The parameter updating is guided via the Kullback-Leibler divergence between the multivariate Normal and the Boltzmann densities. This approach makes sense because it is well-known that the Boltzmann function yields a reliable model to simulate particles near to optimum locations. Three main contributions are presented here in order to build an effective EDA. The first one is a natural gradient formula which allows for an update of the parameters of a density function. These equations are related to an exponential parametrization of the search distribution. The second contribution involves the approximation of the developed gradient formula and its connection to the importance sampling method. The third contribution is a parameter update rule which is designed to control the exploration and exploitation phases of the algorithm. The proposed EDA is tested on a benchmark of 16 problems and compared versus the XNES and iAMaLGaM algorithms. The statistical results show that the performance of the proposed method is competitive and it is the winner in several problems.
The protein-ligand docking problem plays an essential role in structure -based drug design. The challenge for a protein-ligand docking method is how to execute an efficient conformational search to explore a well -des...
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The protein-ligand docking problem plays an essential role in structure -based drug design. The challenge for a protein-ligand docking method is how to execute an efficient conformational search to explore a well -designed scoring function. In this study, we improved the artificial bee colony (ABC) algorithm and proposed an approach called ABC-EDM to solve the protein-ligand docking problem. ABC-EDM employs the scoring function of the classical AutoDock Vina to evaluate a solution during docking simulation. ABCEDM adopts the search framework of the canonical ABC algorithm to execute conformational search. By further investigating the characteristics of the protein-ligand docking problem, a proprietary search mechanism inspired by estimation of distribution algorithm, i.e., estimation of distribution mechanism (EDM), is designed to enhance the performance of ABC-EDM. To verify the effectiveness of the proposed ABC-EDM, we compare it with three variants of the ABC algorithm, three evolutionary computation algorithms, and AutoDock Vina. The experimental results show that ABC-EDM can effectively solve the protein-ligand docking problem, and it can achieve a success rate 5% higher than AutoDock Vina on the GOLD dataset. This study reveals that taking advantage of problem -specific information about the protein-ligand docking problem to enhance a docking method contributes to solving this problem.
Former information of probability model and inferior individuals were discarded in the research of estimation of distribution algorithm usually, but they may contain useful information. In this paper, the former proba...
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ISBN:
(纸本)9781450329651
Former information of probability model and inferior individuals were discarded in the research of estimation of distribution algorithm usually, but they may contain useful information. In this paper, the former probability information is introduced to avoid premature convergence caused by continuously select superior individuals of current population tobuilt probability model, and the individual sampling from superior probability model is filtered by inferior probability model to avoid generating inferior individuals. The algorithm is simulated through the widely used knapsack examples, the results verify the validity of the proposed method, and give suggestion for the choice of parameter through simulation and analysis.
In recent decades, estimation of distribution algorithms (EDAs) have gained much popularity in the evolutionary computation community for solving optimization problems. Characterized by the use of probabilistic models...
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In recent decades, estimation of distribution algorithms (EDAs) have gained much popularity in the evolutionary computation community for solving optimization problems. Characterized by the use of probabilistic models to represent the solutions and the interactions between the variables of the problem, EDAs can be applied to either discrete, continuous or mixed domain problems. Due to this robustness, these algorithms have been used to solve a diverse set of real-world and academic optimization problems. However, a straightforward application is only limited to a few cases, and for the general case, an efficient application requires intuition from the problem as well as notable understanding in probabilistic modeling. In this paper, we provide a roadmap for solving optimization problems via EDAs. It is not the aim of the paper to provide a thorough review of EDAs, but to present a guide for those practitioners interested in using the potential of EDAs when solving optimization problems. In order to present a roadmap which is as useful as possible, we address the key aspects involved in the design and application of EDAs, in a sequence of stages: (1) the choice of the codification, (2) the choice of the probability model, (3) strategies to incorporate knowledge about the problem to the model, and (4) balancing the diversification-intensification behavior of the EDA. At each stage, first, the contents are presented together with common practices and advice to follow. Then, an illustration is given with an example which shows different alternatives. In addition to the roadmap, the paper presents current open challenges when developing EDAs, and revises paths for future research advances in the context of EDAs.
As an important class of approximate dynamic programming, the direct heuristic dynamic programming (DHDP) is discussed in this *** performs well due to its model-free online learning *** the classical DHDP is implemen...
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As an important class of approximate dynamic programming, the direct heuristic dynamic programming (DHDP) is discussed in this *** performs well due to its model-free online learning *** the classical DHDP is implemented with gradient-based adaptation learning algorithm of neural network, in this paper we present a design strategy of DHDP with a novel hybrid estimation of distribution algorithm for online learning and control, and the proposed design optimization method achieves the weight training of neural networks with faster convergence *** proposed approach can be viewed as an improvement for *** simulation is conducted on a practical system plant to test the online learning performance by using our ***, the simulation results show the effectiveness of our approach.
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). ...
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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.
This paper proposes a new multiobjective estimation of distribution algorithm (EDA) based on joint probabilistic modeling of objectives and variables. This EDA uses the multidimensional Bayesian network as its probabi...
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This paper proposes a new multiobjective estimation of distribution algorithm (EDA) based on joint probabilistic modeling of objectives and variables. This EDA uses the multidimensional Bayesian network as its probabilistic model. In this way, it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learned between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm find better tradeoff solutions to the multiobjective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multiobjective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is first applied to the set of walking fish group problems, and its optimization performance is compared with a standard multiobjective evolutionary algorithm and another competitive multiobjective EDA. The experimental results show that on several of these problems, and for different objective space dimensions, the proposed algorithm performs significantly better and on some others achieves comparable results when compared with the other two algorithms. The algorithm is then tested on the set of CEC09 problems, where the results show that multiobjective optimization based on joint model estimation is able to obtain considerably better fronts for some of the problems compared with the search based on conventional genetic operators in the state-of-the-art multiobjective evolutionary algorithms.
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