In this paper, a highly parallel approach for solving multicriteria optimization problems is proposed. The considered approach is based on the reduction of the multicriterial problems to the globaloptimization ones u...
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ISBN:
(纸本)9783030365929;9783030365912
In this paper, a highly parallel approach for solving multicriteria optimization problems is proposed. The considered approach is based on the reduction of the multicriterial problems to the globaloptimization ones using the minimax convolution of the partial criteria, the dimensionality reduction with the use of the Peano space-filling curves, and the application of the efficient parallel information-statistical globaloptimization methods. The required computations can be time-consuming since functions representing individual criteria can be multi-extremal and computationally expensive. The proposed approach comprises two different schemes for efficient parallel computations on high performance systems with shared and distributed memory and with a large number of computational units. The computational efficiency is achieved by storing all the computed criteria values and their intensive reuse for finding new solutions. The results of numerical experiments have demonstrated that this approach allows to reduce the computational costs of solving multicriteria optimization problems by a factor between 10 and 100.
Because of the fragility and vulnerability of the satellite navigation system, it is unable to provide continuous and reliable positioning navigation for UAVs in complex regions such as indoor and canyons. This paper ...
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ISBN:
(纸本)9781450372640
Because of the fragility and vulnerability of the satellite navigation system, it is unable to provide continuous and reliable positioning navigation for UAVs in complex regions such as indoor and canyons. This paper presents a combined navigation method based on visual optical flow and inertial navigation. This method uses ORB to realize the factor extraction of images, and improve Lucas-Kanade method by using the whole optimization method. Combining optical flow and inertial navigation based on extending the Kalman filter. The result of simulation experience shows that the evaluated error of the arithmetic presented in this paper is 0.08m/s, which can satisfice the Indoor integrated navigation of unmanned aerial vehicle (UAV).
In this paper, we consider a two-way cognitive relay network comprising two sources and multiple relays. The relays use a simple Amplify-and-Forward relaying mechanism. For such networks, we formulate the max-min and ...
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ISBN:
(纸本)9781457711800
In this paper, we consider a two-way cognitive relay network comprising two sources and multiple relays. The relays use a simple Amplify-and-Forward relaying mechanism. For such networks, we formulate the max-min and sum capacity optimization problems. The formulated optimization problems are non-convex and nonlinear in nature. We obtain the optimal solution of the optimization problems by using a known technique called global optimization algorithm (GOP). We note that the computational complexity of the GOP algorithm grows exponentially with the number of relays. Therefore, we propose low-complexity heuristics that provide suboptimal solutions to the given optimization problems. The simulation results show that the performance of the heuristics is close to that of the respective optimal solutions.
In this study, a new hybrid algorithm, hDEBSA, is proposed with the aid of two evolutionary algorithms, Differential Evolution (DE) and Backtracking Search optimizationalgorithm (BSA). The control parameters of both ...
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In this study, a new hybrid algorithm, hDEBSA, is proposed with the aid of two evolutionary algorithms, Differential Evolution (DE) and Backtracking Search optimizationalgorithm (BSA). The control parameters of both algorithms are simultaneously considered as a self-adaptation basis such that the values of the parameters update automatically during the optimization process to improve performance and convergence speed. To validate the proposed algorithm, twenty-eight CEC2013 test functions are considered. The performance results of hDEBSA are validated by comparing them with several state-of-the-art algorithms that are available in literature. Finally, hDEBSA is applied to solve four real-world optimization problems, and the results are compared with the other algorithms, where it was found that the hDEBSA performance is better than that of the other algorithms.
This paper proposes an efficient method for solving complex multicriterial optimization problems, for which the optimality criteria may be multiextremal and the calculations of the criteria values may be time-consumin...
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This paper proposes an efficient method for solving complex multicriterial optimization problems, for which the optimality criteria may be multiextremal and the calculations of the criteria values may be time-consuming. The approach involves reducing multicriterial problems to globaloptimization ones through minimax convolution of partial criteria, reducing dimensionality by using Peano curves and implementing efficient information-statistical methods for globaloptimization. To efficiently find the set of Pareto-optimal solutions, it is proposed to reuse all the search information obtained in the course of optimization. The results of computational experiments indicate that the proposed approach greatly reduces the computational complexity of solving multicriterial optimization problems.
During the past two decades, many computational tools were developed to aid novel biochemical pathway design. However, when longer pathways are to be predicted, putative reaction network with a large number of compoun...
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Constrained optimization problems in mechanical engineering are very difficult for the optimizationalgorithm. In 2013, an improved version of constrained differential evolution, named ArATM-ICDE was proposed to optim...
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ISBN:
(纸本)9781538675731
Constrained optimization problems in mechanical engineering are very difficult for the optimizationalgorithm. In 2013, an improved version of constrained differential evolution, named ArATM-ICDE was proposed to optimize the constrained optimization problem. An archiving-based adaptive trade-off model (ArATM) was constructed to handle the constraints;resulting in an algorithm referred to as ArATM-ICDE. This paper applies ArATM-ICDE to solve constraint optimization problems in mechanical engineering. We also combine the penalty technique for constraint handling into the ICDE, named Penalty-ICDE;which compares the abilities of the constraint handling techniques. Our experiments were conducted on ten widely used constraint engineering optimization problems. The experiment results proved the ArATM-ICDE to be more reliable than the Penalty-ICDE. Additionally, ArATM-ICDE consumed a lesser number of function calls than Penalty-ICDE. This paper further compared the effectiveness of ArATM-ICDE and Penalty-ICDE with eight state-of-the-art algorithms, which revealed that ArATM-ICDE and Penalty-ICDE produced solutions of higher quality than those produced by the comparative algorithms. The ArATM-ICDE also consumed less effort in its process.
We consider a discrete intermodal network design problem for freight transportation, in which the network planner needs to determine whether or not to build up or expand a link to minimize the total operating cost of ...
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We consider a discrete intermodal network design problem for freight transportation, in which the network planner needs to determine whether or not to build up or expand a link to minimize the total operating cost of carriers and hub operators under a general route choice model of intermodal operators. We formulate the problem as a mixed-integer nonlinear and non-convex program that involves congestion effects, piecewise linear cost functions, and a fixed-point constraint. We develop a series of relaxed and equivalent models to reduce the hardness of the problem and provide theoretical results to show the equivalences. We present two solution methods to solve the problem with one returning heuristic solutions and the other generating a globally optimal solution. We offer two numerical experiments to test the two solution algorithms and also shed light on their performance comparisons. (C) 2016 Elsevier Ltd. All rights reserved.
Assimilating external data into crop growth model to improve accuracy of crop growth monitoring and yield estimation has been being a research hotspot in recent years. In this paper, the global optimization algorithm ...
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ISBN:
(纸本)9781424495665
Assimilating external data into crop growth model to improve accuracy of crop growth monitoring and yield estimation has been being a research hotspot in recent years. In this paper, the global optimization algorithm SCE-UA (Shuffled Complex Evolution method - University of Arizona) was used to integrate remotely sensed leaf area index (LAI) with crop growth model EPIC to simulate regional yield, sowing date, plant density and net nitrogen fertilizer application rate of summer maize in Huanghuaihai Plain. The final results showed that average relative error of estimated summer maize yield was 4.37% and RMSE was 0.44t/ha. Meanwhile, compared with actual observation and investigation data, average relative error of simulated sowing date, plant density and net N fertilization application rate was 1.85%, -7.78% and -10.60% respectively. These above accuracy of simulated results could meet the need of crop monitoring at regional scale. It was proved that integrating remotely sensed LAI with EPIC model based on global optimization algorithm SCE-UA for simulation of crop growth condition and crop yield was feasible.
Background: A relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the...
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Background: A relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site since the conformation, size and chemical composition of the protein surface are all relevant for the interaction with a specific ligand. Several matching strategies have been designed for the recognition of protein-ligand binding sites and of protein-protein interfaces but the problem cannot be considered solved. Results: In this paper we propose a new method for local structural alignment of protein surfaces based on continuous globaloptimization techniques. Given the three-dimensional structures of two proteins, the method finds the isometric transformation (rotation plus translation) that best superimposes active regions of two structures. We draw our inspiration from the well-known Iterative Closest Point (ICP) method for three-dimensional (3D) shapes registration. Our main contribution is in the adoption of a controlled random search as a more efficient globaloptimization approach along with a new dissimilarity measure. The reported computational experience and comparison show viability of the proposed approach. Conclusions: Our method performs well to detect similarity in binding sites when this in fact exists. In the future we plan to do a more comprehensive evaluation of the method by considering large datasets of non-redundant proteins and applying a clustering technique to the results of all comparisons to classify binding sites.
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