In this paper a short overview and a case study in a statistical comparison of stochastic optimizationalgorithms are presented. The algorithms are part of the Black-Box optimization Benchmarking 2015 competition that...
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The general-purpose graphic processing unit (GPGPU) is a popular accelerator for general applications such as scientific computing because the applications are massively parallel and the significant power of parallel ...
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The general-purpose graphic processing unit (GPGPU) is a popular accelerator for general applications such as scientific computing because the applications are massively parallel and the significant power of parallel computing inheriting from GPUs. However, distributing workload among the large number of cores as the execution configuration in a GPGPU is currently still a manual trial-and-error process. Programmers try out manually some configurations and might settle for a sub-optimal one leading to poor performance and/or high power consumption. This paper presents an auto-tuning approach for GPGPU applications with the performance and power models. First, a model-based analytic approach for estimating performance and power consumption of kernels is proposed. Second, an auto-tuning framework is proposed for automatically obtaining a near-optimal configuration for a kernel computation. In this work, we formulated that automatically finding an optimal configuration as the constraint optimization and solved it using either simulated annealing (SA) or genetic algorithm (GA). Experiment results show that the fidelity of the proposed models for performance and energy consumption are 0.86 and 0.89, respectively. Further, the optimizationalgorithms result in a normalized optimality offset of 0.94% and 0.79% for SA and GA, respectively. (C) 2015 Elsevier B.V. All rights reserved.
Hitting Set is a classic problem in combinatorial optimization. Its input consists of a set system F over a finite universe U and an integer t;the question is whether there is a set of t elements that intersects every...
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This paper proposes an application prototype for forecasting of stock prices using feed-forward neural network with back propagation, Particle Swarm optimization and Differential Evolution. The prototype provides a co...
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ISBN:
(纸本)9781509027750
This paper proposes an application prototype for forecasting of stock prices using feed-forward neural network with back propagation, Particle Swarm optimization and Differential Evolution. The prototype provides a convenient graphical user interface that allows choosing stocks, period of data, percentage of training set, technical indicators for model inputs and other algorithmic parameters. Multithreading is provided for efficient running and the downloaded historical data and forecasted output can be save for future use. An experiment was performed to investigate the performance of the three algorithms as well as the effects of number of hidden nodes of the neural networks.
Wireless power transfer (WPT) is expected to be a technology reshaping the landscape of low-power applications such as the Internet of Things, machine-to-machine communications and radio frequency identification netwo...
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ISBN:
(纸本)9781509017492
Wireless power transfer (WPT) is expected to be a technology reshaping the landscape of low-power applications such as the Internet of Things, machine-to-machine communications and radio frequency identification networks. Although there has been some progress towards multi-antenna multi-sine WPT design, the large-scale design of WPT, reminiscent of massive multiple-input multiple-output (MIMO) in communications, remains an open problem. Considering the nonlinear rectifier model, a multiuser waveform optimization algorithm is derived based on successive convex approximation (SCA). A lower-complexity algorithm is derived based on asymptotic analysis and sequential approximation (SA). It is shown that the difference between the average output voltage achieved by the two algorithms can be negligible provided the number of antennas is large enough. The performance gain of the nonlinear model based design over the linear model based design can be large, in the presence of a large number of tones(1).
作者:
Kilani, Y.Alsarhan, A.Bsoul, M.Otoom, A. F.Hashemite Univ
Fac Prince Al Hussein Bin Abdullah II Informat Te Dept Comp Informat Syst Zarqa Jordan Hashemite Univ
Fac Prince Al Hussein Bin Abdullah II Informat Te Dept Comp Sci & Applicat Zarqa Jordan Hashemite Univ
Fac Prince Al Hussein Bin Abdullah II Informat Te Dept Software Engn Zarqa Jordan
Local search is a metaheuristic for solving computationally hard optimization problems. In the past three decades, local search has grown from a simple heuristic idea into a mature field of research in combinatorial o...
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ISBN:
(纸本)9783319182964
Local search is a metaheuristic for solving computationally hard optimization problems. In the past three decades, local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization that is attracting ever-increasing attention. It is still the method of choice for NP-hard problems as it provides a robust approach for obtaining high-quality solutions to problems of a realistic size in reasonable time. optimization problems such as the shortest path, the traveling salesman, pin packing, and the Knapsack problems. Local search techniques have been successful in solving large and tight constraint satisfaction problems. Local search algorithms turn out to be effective in solving many constraint satisfaction problems. This chapter gives an introduction to the local search algorithms, the optimization and the constraint satisfaction problems, and the local search methods used to solve them.
This book constitutes the refereed proceedings of the 12th IFIP WG 12.5 international Conference on Artificial Intelligence applications and Innovations, AIAI 2016, and three parallel workshops, held in Thessaloniki, ...
ISBN:
(数字)9783319449449
ISBN:
(纸本)9783319449432
This book constitutes the refereed proceedings of the 12th IFIP WG 12.5 international Conference on Artificial Intelligence applications and Innovations, AIAI 2016, and three parallel workshops, held in Thessaloniki, Greece, in September 2016. The workshops are the Third workshop on New Methods and Tools for Big Data, MT4BD 2016, the 5th Mining Humanistic Data workshop, MHDW 2016, and the First workshop on 5G - Putting Intelligence to the Network Edge, 5G-PINE 2016. The 30 revised full papers and 8 short papers presented at the main conference were carefully reviewed and selected from 65 submissions. The 17 revised full papers and 7 short papers presented at the 3 parallel workshops were selected from 33 submissions. The papers cover a broad range of topics such as artificial neural networks, classification, clustering, control systems - robotics, data mining, engineering application of AI, environmental applications of AI, feature reduction, filtering, financial-economics modeling, fuzzy logic, genetic algorithms, hybrid systems, image and video processing, medical AI applications, multi-agent systems, ontology, optimization, pattern recognition, support vector machines, text mining, and Web-social media data AI modeling.
To study the behavior of the transformers and the effects of these devices on power network performance in transient states, various models with different objectives are presented. One of the most important objectives...
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ISBN:
(纸本)9783319184166;9783319184159
To study the behavior of the transformers and the effects of these devices on power network performance in transient states, various models with different objectives are presented. One of the most important objectives is to investigate the overvoltages made in transformer as a result of lightning waves. For this reason, a black box model applied in the analysis of transient state of the transformer high frequency (up to 1.2 MHz). As a new work, the Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Imperialist Competitive Algorithm (ICA) are proposed in the estimation of parameters of black box model using the necessary measurements on three-phase transformer 2.5 MVA and 6300/420 V and then compared by analytical methods.
Similarly to Maximum Satisfiability (MaxSAT), Minimum Satisfiability (MinSAT) is an optimization extension of the Boolean Satisfiability (SAT) decision problem. In recent years, both problems have been studied in term...
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Similarly to Maximum Satisfiability (MaxSAT), Minimum Satisfiability (MinSAT) is an optimization extension of the Boolean Satisfiability (SAT) decision problem. In recent years, both problems have been studied in terms of exact and approximation algorithms. In addition, the MaxSAT problem has been characterized in terms of Maximal Satisfiable Subsets (MSSes) and Minimal Correction Subsets (MCSes), as well as Minimal Unsatisfiable Subsets (MUSes) and minimal hitting set dualization. However, and in contrast with MaxSAT, no such characterizations exist for MinSAT. This paper addresses this issue by casting the MinSAT problem in a more general framework. The paper studies Maximal Falsifiability, the problem of computing a subset-maximal set of clauses that can be simultaneously falsified, and shows that MinSAT corresponds to the complement of a largest subset-maximal set of simultaneously falsifiable clauses, i.e. the solution of the Maximum Falsifiability (MaxFalse) problem. Additional contributions of the paper include novel algorithms for Maximum and Maximal Falsifiability, as well as minimal hitting set dualization results for the MaxFalse problem. Moreover, the proposed algorithms are validated on practical instances.
Nowadays, wireless access networks are already amongst the top power consumers in the ICT (Information and Communication Technology) sector. As it expected that these networks will further expand in the future due to ...
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ISBN:
(纸本)9781509020881
Nowadays, wireless access networks are already amongst the top power consumers in the ICT (Information and Communication Technology) sector. As it expected that these networks will further expand in the future due to the extreme growth in mobile devices and the high bit rate demand of the applications running on these devices, it is important to consider power consumption as a key parameter in the network design phase. In this paper, two optimizationalgorithms are proposed: a capacity-based heuristic which aims to reduce power consumption by responding to the instantaneous bit rate demand by the user and an evolutionary opposition-based learning algorithm focusing on the joint-optimization of power consumption and geometrical coverage. Applying both algorithms on a realistic suburban case in Ghent, Belgium, show that both algorithms are able to design an LTE-A network consuming only 24% and 29%, respectively, of the power consumed by the reference scenario which is representative for today's networks. The evolutionary algorithm outperforms the capacity-based algorithm by obtaining a 5% lower power consumption, while the capacity-based heuristic has a 2 to 3% higher coverage. Future research in joint-optimizationalgorithms of energy and network performance is definitely needed.
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