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7th international Conference on Bioinspired optimization Methods and their applications, BIOMA 2016

作者： Eftimov, Tome Korošec, Peter Seljak, Barbara Koroušić Computer Systems Department Jožef Stefan Institute Ljubljana Slovenia Jožef Stefan International Postgraduate School Ljubljana Slovenia

ISBN: (纸本)9789612640934

In this paper a short overview and a case study in a statistical comparison of stochastic optimization algorithms are presented. The algorithms are part of the Black-Box optimization Benchmarking 2015 competition that was held at the 5th GECCO workshop for Real-Parameter optimization. The question about the difference between parametric and non-parametric tests for single-problem analysis and for multiple-problem analysis is addressed in this paper. The main contributions are the disadvantages that can appear by using multiple-problem analysis, in the case when the data of some algorithms includes outliers. © Proceedings of the 7th international Conference on Bioinspired optimization Methods and their applications, BIOMA 2016. All rights reserved.

关键词： optimization

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Auto-tuning for GPGPU applications using performance and energy model

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JOURNAL OF SYSTEMS ARCHITECTURE 2016年第1期62卷 40-53页

作者： Lin, Chih-Sheng Teng, Shih-Meng Hsiung, Pao-Ann Ind Technol Res Inst Informat & Commun Res Labs Hsinchu Taiwan Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chaiyi Taiwan

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 optimization algorithms 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.

关键词： GPGPU Performance and Energy modeling Auto-tuning optimization

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On structural parameterizations of hitting set: Hitting paths in graphs using 2-SAT

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Journal of Graph algorithms and applications 2017年第2期21卷 219-245页

作者： Jansen, Bart M. P. Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

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 set in F. The Hitting Set problem parameterized by the size of the solution is a well-known W[2]-complete problem in parameterized complexity theory. In this paper we investigate the complexity of Hitting Set under various structural parameterizations of the input. Our starting point is the folklore result that Hitting Set is polynomial-time solvable if there is a tree T on vertex set U such that the sets in F induce connected subtrees of T. We consider the case that there is a treelike graph with vertex set U such that the sets in F induce connected subgraphs;the parameter of the problem is a measure of how treelike the graph is. Our main positive result is an algorithm that, given a graph G with cyclomatic number k, a collection P of simple paths in G, and an integer t, determines in time 25k(|G| + |P|)O(1)whether there is a vertex set of size t that hits all paths in P. It is based on a connection to the 2-SAT problem in multiple valued logic. For other parameterizations we derive W[1]-hardness and para-NP-completeness results. © 2017, Brown University. All rights reserved.

关键词： Combinatorial optimization

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A Stock Price Forecasting Application using Neural Networks with Multi-Optimizer 9

A Stock Price Forecasting Application using Neural Networks ...

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9th IEEE international workshop on Computational Intelligence and applications (IWCIA)

作者： Worasucheep, Chukiat KMUTT Fac Sci Dept Math Appl Comp Sci Program Bangkok Thailand

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.

关键词： Artificial neural network back propagation hybrid algorithm particle swarm optimization stock forecasting application

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Waveform optimization for Large-Scale Multi-Antenna Multi-Sine Wireless Power Transfer 17

Waveform Optimization for Large-Scale Multi-Antenna Multi-Si...

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17th IEEE international workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Huang, Yang Clerckx, Bruno Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Korea Univ Sch Elect Engn Seoul South Korea

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).

关键词： Wireless power transfer nonlinear model massive MIMO convex optimization

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Local Search algorithms for Solving the Combinatorial optimization and Constraint Satisfaction Problems 6th

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6th international workshop on Soft Computing applications (SOFA)

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

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.

关键词： Local search Introduction Combinatorial optimization problems Constraint satisfaction problems

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Artificial Intelligence applications and Innovations 1st ed. 2016

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丛书名： IFIP Advances in Information and Communication Technology

2016年

作者： Lazaros Iliadis Ilias Maglogiannis

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.

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Applying Intelligent optimization algorithms for Evaluation of Transformer Black Box Model 6th

Applying Intelligent Optimization Algorithms for Evaluation ...

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6th international workshop on Soft Computing applications (SOFA)

作者： Bigdeli, Mehdi Valii, Mohammad Azizian, Davood Islamic Azad Univ Zanjan Branch Dept Elect Engn Zanjan Iran Islamic Azad Univ Abhar Branch Dept Elect Engn Abhar Iran

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.

关键词： Transformer Transient analysis Black box model Intelligent algorithms

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Maximal falsifiability

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AI COMMUNICATIONS 2016年第2期29卷 351-370页

作者： Ignatiev, Alexey Morgado, Antonio Planes, Jordi Marques-Silva, Joao Univ Lisbon IST INESC ID P-1699 Lisbon Portugal Univ Lleida Lleida Spain UCD CASL Dublin Ireland

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.

关键词： Maximum falsifiability minimum satisfiability minimal hitting set duality Boolean optimization

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Optimizing Wireless Access Networks towards Power Consumption: Influence of the optimization Algorithm 14

Optimizing Wireless Access Networks towards Power Consumptio...

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international workshop on Parallel optimization using/for Multi and Many-Core High Performance Computing (POMCO) in conjunction with 14th international Conference on High Performance Computing and Simulation (HPCS)

作者： Deruyck, Margot Plets, David Martens, Luc Joseph, Wout Goudos, Sotirios K. Univ Ghent iMinds WAVES Dept Informat Technol Ghent Belgium Aristotle Univ Thessaloniki Dept Phys Thessaloniki Greece

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 optimization algorithms 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-optimization algorithms of energy and network performance is definitely needed.

关键词： energy efficiency LTE-A network design optimization algorithm power consumption wireless access networks

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