The multivariate linear errors-in-variables (EIV) model is frequently used in computer vision for model fitting tasks. As well known, when sample data is contaminated by large numbers of awkwardly placed outliers, the...
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ISBN:
(纸本)9783037853559
The multivariate linear errors-in-variables (EIV) model is frequently used in computer vision for model fitting tasks. As well known, when sample data is contaminated by large numbers of awkwardly placed outliers, the least squares estimator isn't robust. To obtain robust estimators of multivariate linear EIV model, orthogonal least trimmed square and orthogonal least trimmed absolute deviation estimators based on the subset of h cases(out of n)are proposed. However, these robust estimators possessing the exact fit property are NP-hard to compute. To tackle this problem, an integer-coded genetic algorithm that is applicable to trimmed estimators is presented. The trimmed estimators of multivariate linear EIV model on real data are provided and the results show that the integer-coded genetic algorithm is correct and effective.
Train arrivals and departures should be scheduled over a certain period when talking about train timetabling problems. For the midnight train operations, passengers significantly concern about the network transfer iss...
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Train arrivals and departures should be scheduled over a certain period when talking about train timetabling problems. For the midnight train operations, passengers significantly concern about the network transfer issue. Currently, some existing studies address the last train timetabling problem by only optimizing the timetable for the last train on a single subway line whereas this study takes into consideration the complete last-shift period. We first put forward a last-shift train scheduling model aiming to minimize the transfer waiting time and maximize the network connectivity. Two genetic-based algorithms, an integer-coded genetic algorithm (ICGA) and a binary-codedgeneticalgorithm (BCGA) are developed. The relevance and applicability of the algorithms have been demonstrated by several testing networks and real-world implementation. The ICGA and the branch-and-bound approaches show high efficiency in obtaining the optimal solutions for a small network, while the BCGA approach that bases on an integer-programming model shows low efficiency in addressing problems of sparse solution spaces. However, the branch-and-bound approach has limited ability in solving medium-sized networks. On the contrary, the ICGA generates satisfactory results in solution quality and computational efficiency when applied to large-sized networks. (C) 2021 Elsevier B.V. All rights reserved.
This paper proposes a new integer-coded genetic algorithm (ICGA) for the solution of the thermal unit commitment problem. The thermal generating units scheduling consists of the sequence of operation/reservation times...
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This paper proposes a new integer-coded genetic algorithm (ICGA) for the solution of the thermal unit commitment problem. The thermal generating units scheduling consists of the sequence of operation/reservation times of the generating units, which is coded into a sequence of alternating sign integer numbers in the proposed ICGA. The minimum up and down time constraints of the generating units are directly coded in the chromosome structure of the ICGA. The proposed ICGA has a new hybrid crossover composed of modified average bound and swapping operators. In addition, a combination of uniform and non-uniform mutations is used as the mutation operator. As a result, the algorithm robustness is improved. Test results with systems of up to 300 units and 24 hours scheduling horizon are presented. The comparison of the obtained results with those of other Unit Commitment (UC) methods justifies the effectiveness of the proposed method in light of minimizing the total operation cost. Copyright (C) 2008 John Wiley & Sons, Ltd.
The needs for location-aware applications are increasing with the popularity of ubiquitous mobile computing. However, as an important factor affecting the positioning performance, the deployment of anchor nodes has re...
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ISBN:
(纸本)9781479973392
The needs for location-aware applications are increasing with the popularity of ubiquitous mobile computing. However, as an important factor affecting the positioning performance, the deployment of anchor nodes has received little attention during the past years. In this paper we investigate the anchor placement in the received signal strength (RSS) based wireless localization systems. An optimization method based on integer-coded genetic algorithm is proposed. It takes the average localization error and the signal coverage estimate as a joint optimization criterion. The influence of RSS fluctuation on the system is also considered in the algorithm. We evaluate the performance of our proposed algorithm in simulations and the results are discussed in details.
This paper presents a new method with integration of generation and transmission networks reliability for the solution of unit commitment (UC) problem. In fact, in order to have a more accurate assessment of system re...
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This paper presents a new method with integration of generation and transmission networks reliability for the solution of unit commitment (UC) problem. In fact, in order to have a more accurate assessment of system reserve requirement, in addition to unavailability of generation units, unavailability of transmission lines are also taken into account. In this way, evaluation of the required spinning reserve (SR) capacity is performed by applying reliability constraints based on loss of load probability and expected energy not supplied (EENS) indices. Calculation of the above parameters is accomplished by employing a novel procedure based on the linear programming which it also minimizes them to achieve optimum level of the SR capacity and consequently a cost-benefit reliability constrained UC schedule. In addition, a powerful solution technique called "integer-coded genetic algorithm (ICGA)" is being used for the solution of the proposed method. Numerical results on the IEEE reliability test system show that the consideration of transmission network unavailability has an important influence on reliability indices of the UC schedules. (C) 2008 Elsevier Ltd. All rights reserved.
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