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15th Annual International Conference on Computational Science (ICCS)

作者： Blecic, Ivan Cecchini, Arnaldo Trunfio, Giuseppe A. Univ Sassari Dept Architecture Planning & Design Alghero Italy

In the paper we present an urban design support tool centered on pedestrian accessibility and walkability of places. Differently from standard decision support systems developed for the purpose of evaluating given pre-defined urban projects and designs, we address the inverse problem of having the software system itself generate hypotheses of projects and designs, given some (user-provided) objectives and constraints. Taking as a starting point a model for evaluating walkability, we adapt the NSGA-II multi-objective genetic algorithm to produce the front of non-dominated design alternatives to satisfy certain predefined constraints. By way of example, we briefly present an application of the system to a real urban area.

关键词： design support system walkability multiobjective genetic algorithm urban design

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Real-time task scheduling by multiobjective genetic algorithm

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JOURNAL OF SYSTEMS AND SOFTWARE 2009年第4期82卷 619-628页

作者： Yoo, Myungryun Musashi Inst Technol Fac Knowledge Engn Setagaya Ku Tokyo 1588557 Japan

Real-time tasks are characterized by computational activities with timing constraints and classified into two categories: a hard real-time task and a soft real-time task. In hard real-time tasks, tardiness can be catastrophic. The goal of hard real-time tasks scheduling algorithms is to meet all tasks' deadlines, in other words, to keep the feasibility of scheduling through admission control. However, in the case of soft real-time tasks, slight violation of deadlines is not so critical. In this paper, we propose a new scheduling algorithm for soft real-time tasks using multiobjective genetic algorithm (moGA) on multiprocessors system. It is assumed that tasks have precedence relations among them and are executed on homogeneous multiprocessor environment. The objective of the proposed scheduling algorithm is to minimize the total tardiness and total number of processors used. For these objectives, this paper combines adaptive weight approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. The effectiveness of the proposed algorithm is shown through simulation studies. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Soft real-time task multiobjective genetic algorithm

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Towards a Design Support System for Urban Walkability

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Procedia Computer Science 2015年 51卷 2157-2167页

作者： Ivan Blecic Arnaldo Cecchini Giuseppe A. Trunfio Department of Architecture Planning and Design University of Sassari Alghero Italy

关键词： Design support system Walkability multiobjective genetic algorithm Urban design

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multiobjective Optimization of MPLS-IP Networks with a Variable Neighborhood genetic algorithm

Multiobjective Optimization of MPLS-IP Networks with a Varia...

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作者： Renata da Encarnação Onety Federal University Of Minas Gerais

学位级别：博士

The demand for different levels of Quality of Service (QoS) in IP networks is growing, mainly to attend multimedia applications. However, not only indicators of quality have conflicting features, but also the problem of determining routes covered by more than two QoS constraints is NP-complete (Nondeterministic Polynomial Time Complete). This work proposes an algorithm to optimize multiple Quality of Service indices of Multi Protocol Label Switching (MPLS) IP networks. Such an approach aims at minimizing the network cost and the amount of simultaneous requests rejection, as well as performing load balancing among routes. The proposed algorithm, the Variable Neighborhood multiobjective genetic algorithm (VN-MGA), is a genetic algorithm based on the Elitist Non-Dominated Sorted genetic algorithm (NSGA-II), with a particular feature that different parts of a solution are encoded differently, at Level 1 and Level 2. In order to improve results, both representations are needed. At Level 1, the first part of the solution is encoded by considering as decision variables the arrows that form the routes to be followed by each request (whilst the second part of the solution is kept constant), whereas at Level 2, the second part of the solution is encoded by considering the sequence of requests as decision variables, and first part is kept constant. Pareto-fronts obtained by VN-MGA dominate fronts obtained by fixed-neighborhood encoding schemes. Besides potential benefits of the proposed approach application to packet routing optimization in MPLS networks, this work raises the theoretical issue of the systematic application of variable encodings, which allow variable neighborhood searches, as operators inside general evolutionary computation algorithms.

关键词： Routing multiobjective genetic algorithm Variable encoding

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A nonparametric approach using artificial intelligence in vibration and noise reduction of flexible systems

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2014年第8期228卷 1329-1347页

作者： Zolfagharian, Ali Noshadi, Amin Ghasemi, Seyed Ebrahim Zain, Mohd Zarhamdy Md Univ Teknol Malaysia Dept Syst Dynam & Control Johor Baharu 81310 Malaysia Victoria Univ Sch Sci & Engn Fac Hlth Engn & Sci Melbourne Vic 8001 Australia Babol Univ Technol Dept Mech Engn Babol Sar Iran

The main aim of this paper is to broaden the application's area of artificial intelligence including fuzzy logic and multiobjective evolutionary algorithm into real-time control area. Wiper system is a high order, nonlinear model with single-input and multi-outputs so that rise time, maximum overshoot, and end-point vibration of wiper blade are observed in conflict as the faster response leads to the larger level of undesired noise and vibration. The first part of this paper centers acquiring experimental data from a passenger automobile wiper system during its operation and using a reliable nonlinear system identification, namely, nonlinear autoregressive exogenous Elman neural network. Knowing that in a practical environment, where the loading conditions of the flexible wiper blade may be varied due to rain, snow, or wind lift in high-speed driving, causing changes in the characteristics of the system, the system performance with a fixed conventional controller scheme will not be satisfactory. The main contribution of this work is presented in second part where a novel multiobjective, bilevel adaptive-fuzzy controller is proposed for an automobile wiper system. The system's parameters are tuned simultaneously by a multiobjective genetic algorithm based on fitness sharing whereby an automobile wiper blade is moved within its sweep workspace in the least amount of time with minimum noise and vibration.

关键词： Automotive wiper nonlinear system identification fuzzy logic iterative learning multiobjective genetic algorithm

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Complex componential approach for redundancy allocation problem solved by simulation-optimization framework

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JOURNAL OF INTELLIGENT MANUFACTURING 2014年第4期25卷 661-680页

作者： Mariano, Carlos Henrique Fernando Kuri-Morales, Angel UTFPR Curitiba Parana Brazil Univ Nacl Autonoma Mexico Mexico City DF Mexico ITAM IIMAS UNAM San Angel Mexico City DF Mexico

This article addresses the problem of redundancy and reliability allocation in the operational dimensioning of an automated production system. The aim of this research is to improve the global reliability of the system by allocating alternative components (redundancies) that are associated in parallel with each original component. By considering a complex componential approach that simultaneously evaluates the interrelations among sub-systems, conflicting goals, and variables of different natures, a solution for the problem is proposed through a multi-objective formulation that joins a multi-objective elitist genetic algorithm with a high-level simulation environment also known as simulation optimization framework.

关键词： Reliability optimization Redundancy allocation problem Simulation optimization framework multiobjective optimization multiobjective genetic algorithm

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Combined DVFS and Mapping Exploration for Lifetime and Soft-Error Susceptibility Improvement in MPSoCs

Combined DVFS and Mapping Exploration for Lifetime and Soft-...

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Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Das, A. Kumar, A. Veeravalli, B. Bolchini, C. Miele, A. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore Politecn Milan Dip Elettron Informaz & Bioingn I-20133 Milan Italy

ISBN: (纸本)9783981537024

Energy and reliability optimization are two of the most critical objectives for the synthesis of multiprocessor systems-on-chip (MPSoCs). Task mapping has shown significant promise as a low cost solution in achieving these objectives as standalone or in tandem as well. This paper proposes a multiobjective design space exploration to determine the mapping of tasks of an application on a multiprocessor system and voltage/frequency level of each tasks (exploiting the DVFS capabilities of modern processors) such that the reliability of the platform is improved while fulfilling the energy budget and the performance constraint set by system designers. In this respect, the reliability of a given MPSoC platform incorporates not only the impact of voltage and frequency on the aging of the processors (wear-out effect) but also on the susceptibility to soft-errors - a joint consideration missing in all existing works in this domain. Further, the proposed exploration also incorporates soft-error tolerance by selective replication of tasks, making the proposed approach an interesting blend of reactive and proactive fault-tolerance. The combined objective of minimizing core aging together with the susceptibility to transient faults under a given performance/energy budget is solved by using a multi-objective genetic algorithm exploiting tasks' mapping, DVFS and selective replication as tuning knobs. Experiments conducted with real-life and synthetic application graphs clearly demonstrate the advantage of the proposed approach.

关键词： fault tolerance genetic algorithms multiprocessing systems power aware computing radiation hardening (electronics) system-on-chip DVFS MPSoCs core aging minimization multiobjective design space exploration multiobjective genetic algorithm multiprocessor systems-on-chip proactive fault-tolerance reactive fault-tolerance reliability optimization selective tasks replication soft-error susceptibility improvement soft-error tolerance synthetic application graphs task mapping wear-out effect Aging Computer architecture Engines Fault tolerance Fault tolerant systems Transient analysis

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Community Detection in Multidimensional Networks 26

Community Detection in Multidimensional Networks

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26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Amelio, Alessia Pizzuti, Clara Natl Res Council Italy CNR Inst High Perf Comp & Net ICAR I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9781479965731

The paper proposes a new approach to detect shared community structure in multidimensional networks based on the combination of multiobjective genetic algorithms, local search, and the concept of temporal smoothness, coming from evolutionary clustering. A multidimensional network is clustered by running on each slice a multiobjective genetic algorithm that maximizes the modularity on such a slice and, at the same time, minimizes the difference between the community structure obtained for the current layer and that found on the already considered dimensions. Experiments on synthetic and real-world datasets show the ability of the approach in discovering latent shared clustering of objects.

关键词： multidimensional networks social networks community detection evolutionary computation multiobjective genetic algorithm

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A MULTI-OBJECTIVE genetic algorithm USING INTERMEDIATE FEATURES OF SIMULATIONS

A MULTI-OBJECTIVE GENETIC ALGORITHM USING INTERMEDIATE FEATU...

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Winter Simulation Conference

作者： Muta, Hidemasa Raymond, Rudy Hara, Satoshi Morimura, Tetsuro IBM Res Tokyo 5-6-52 Toyosu Koto Tokyo 1358511 Japan

ISBN: (纸本)9781479974863

This paper proposes using intermediate features of traffic simulations in a genetic algorithm designed to find the best scenarios in regulating traffic with multiple objectives. A challenge in genetic algorithms for multi-objective optimization is how to find various optimal scenarios within a limited decision time. Typical evolutionary algorithms usually maintain a population of diversified scenarios whose diversity is measured only by the final objectives available at the end of their simulations. We propose measuring the diversity by also the time series of the objectives during the simulations. The intuition is that simulation scenarios with similar final objective values may contain different series of discrete events that, when combined, can result in better scenarios. We provide empirical evidence by experimenting with agent-based traffic simulations showing the superiority of the proposed genetic algorithm over standard approaches in approximating Pareto fronts.

关键词： Pareto optimisation genetic algorithms road traffic control Pareto front decision time diversity measurement evolutionary algorithm multiobjective genetic algorithm traffic regulation traffic simulation feature Cities and towns genetic algorithms Optimization Roads Sociology Statistics Traffic control

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A Methodology for Building Fuzzy Rule-based Systems Integrating Expert and Data Knowledge 3

A Methodology for Building Fuzzy Rule-based Systems Integrat...

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3rd Brazilian Conference on Intelligent Systems (BRACIS)

作者： de Lima, Helano Povoas Camargo, Heloisa de Arruda Fed Univ Sao Carlos UFSCar Dept Comp Sci Sao Carlos SP Brazil

ISBN: (纸本)9781479956180

Historically, since Mamdani proposed his model of fuzzy rule-based system, a lot has changed in the construction process of this type of models. For a long time, the research efforts were directed towards the automatic construction of accurate models starting from data, making fuzzy systems almost mere function approximators. Realizing that this approach escapes from the original concept of fuzzy theory, more recently, researchers attention - focused on the automatic construction of more interpretable models. However, such models, although interpretable, can still not make sense to the expert. This paper proposes an interactive methodology for constructing fuzzy rulebased systems, which aims to integrate the knowledge extracted from experts and induced from data, hoping to contribute to the solution of the mentioned problem.

关键词： fuzzy system construction methodology domain expert multiobjective genetic algorithm

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