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Proceedings of the 19th International Conference on Computer-Aided Architectural Design Research in Asia CAADRIA

作者： Chian, Eugene Janssen, Patrick Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9789881902658

multi-objective evolutionary algorithms have been successfully applied within various design domains in order to explore the trade-offs between conflicting design criteria. This research investigates how evolutionary algorithms can be used to develop urban configurations for walkable new towns, focusing in particular on the trade-off between travelling time using public transport and accessibility to open space. A population of optimised urban configurations was evolved and analysed, resulting in the identification of three differing typologies for walkable new towns.

关键词： Urban structure transportation network urban density multi-objective evolutionary algorithms

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Discovering gene association networks by multi-objective evolutionary quantitative association rules

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2014年第1期80卷 118-136页

作者： Martinez-Ballesteros, M. Nepomuceno-Chamorro, I. A. Riquelme, J. C. Univ Seville Dept Comp Sci Seville Spain

In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Data mining multi-objective evolutionary algorithms Quantitative association rules Gene networks Microarray analysis

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On the Performance of multiple objective evolutionary algorithms for Software Architecture Discovery 14

On the Performance of Multiple Objective Evolutionary Algori...

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16th Genetic and evolutionary Computation Conference (GECCO)

作者： Ramirez, Aurora Raul Romero, Jose Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

ISBN: (纸本)9781450326629

During the design of complex systems, software architects have to deal with a tangle of abstract artefacts, measures and ideas to discover the most fitting underlying architecture. A common way to structure these systems is in terms of their interacting software components, whose composition and connections need to be properly adjusted. Its abstract and highly combinatorial nature increases the complexity of the problem. In this scenario, Search-based Software Engineering (SBSE) may serve to support this decision making process from initial analysis models, since the discovery of component-based architectures can be formulated as a challenging multiple optimisation problem, where different metrics and configurations can be applied depending on the design requirements and its specific domain. Many-objective optimisation evolutionary algorithms can provide an interesting alternative to classical multi-objective approaches. This paper presents a comparative study of five different algorithms, including an empirical analysis of their behaviour in terms of quality and variety of the returned solutions. Results are also discussed considering those aspects of concern to the expert in the decision making process, like the number and type of architectures found. The analysis of many-objectives algorithms constitutes an important challenge, since some of them have never been explored before in SBSE.

关键词： Search-based Software Engineering component-based software architecture multi-objective evolutionary algorithms many-objective evolutionary algorithms

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Modelling energy storage systems using Fourier analysis: An application for smart grids optimal management

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APPLIED SOFT COMPUTING 2014年第PartC期14卷 469-481页

作者： Di Silvestre, Maria Luisa Sanseverino, Eleonora Riva Univ Palermo DEIM I-90128 Palermo Italy

In this paper, a new and efficient model for variables representation, named F-coding, in optimal power dispatch problems for smart electrical distribution grids is proposed. In particular, an application devoted to optimal energy dispatch of Distributed Energy Resources including ideal storage devices is here considered. Electrical energy storage systems, such as any other component that must meet an integral capacity constraint in optimal dispatch problems, have to show the same energy level at the beginning and at the end of the considered timeframe for operation. The use of zero-integral functions, such as sinusoidal functions, for the synthesis of the charge and discharge course of batteries is thus consequential. The issue is common to many other engineering problems, such as any dispatch problem where resources must be allocated within a given amount in a considered timeframe. Many authors have proposed different methods to deal with such integral constraints in the literature on smart grids management, but all of them do not seem very efficient. The paper is organized as follows. First, the state of the art on the optimal management problem is outlined with special attention to treatment of integral constraints, then the proposed new model for variables representation is described. Finally, the multiobjective optimization method and its application to the optimal dispatch problem considering different variables representations are considered. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Integral constraints Electrical energy storage systems Fourier analysis Optimal dispatch of resources multi-objective evolutionary algorithms Smart grids

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Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design

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ENGINEERING OPTIMIZATION 2014年第8期46卷 1032-1051页

作者： Pholdee, Nantiwat Bureerat, Sujin Khon Kaen Univ Fac Engn Dept Mech Engn Khon Kaen Thailand

In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objective optimization. The optimizer search performance is then improved by integrating a mutation operator of evolution strategies and an approximate gradient into its computational procedure. RPBIL and its variants, along with a number of established multi-objective evolutionary algorithms, are then implemented to solve four multi-objective design problems of trusses. The design problems are posted to minimize structural mass and compliance while fulfilling stress constraints. The comparative results based on a hypervolume indicator show that the proposed hybrid RPBIL is the best performer for the large-scale truss design problems.

关键词： comparative performance multi-objective evolutionary algorithms multi-objective truss optimization hybrid evolutionary algorithms population-based incremental learning

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Entropy-based evaluation function in a multi-objective approach for the investigation of the genetic code robustness

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MEMETIC COMPUTING 2014年第3期6卷 157-170页

作者： de Oliveira, Lariza Laura Tinos, Renato Univ Sao Paulo Dept Comp & Math BR-14049 Ribeirao Preto Brazil

The genetic code is the interface between the genetic information stored in DNA molecules and the proteins. Considering the hypothesis that the genetic code evolved to its current structure, some researches use optimization algorithms to find hypothetical codes to be compared to the canonical genetic code. For this purpose, a function with only one objective is employed to evaluate the codes, generally a function based on the robustness of the code against mutations. Very few random codes are better than the canonical genetic code when the evaluation function based on robustness is considered. However, most codons are associated with a few amino acids in the best hypothetical codes when only robustness is employed to evaluate the codes, what makes hard to believe that the genetic code evolved based on only one objective, i.e., the robustness against mutations. In this way, we propose here to use entropy as a second objective for the evaluation of the codes. We propose a Pareto approach to deal with both objectives. The results indicate that the Pareto approach generates codes closer to the canonical genetic code when compared to the codes generated by the approach with only one objective employed in the literature.

关键词： Genetic code robustness Genetic algorithms Computational biology multi-objective evolutionary algorithms

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A multi-objective model for facility location-allocation problem with immobile servers within queuing framework

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COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期74卷 1-10页

作者： Rahmati, Seyed Habib A. Ahmadi, Abbas Sharifi, Mani Chambari, Amirhossain Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran Islamic Azad Univ Fac Ind & Mech Engn Qazvin Branch Qazvin Iran Islamic Azad Univ Young Researcher Club Qazvin Branch Qazvin Iran

This research investigates a practical bi-objective model for the facility location allocation (BOFLA) problem with immobile servers and stochastic demand within the M/M/1/K queue system. The first goal of the research is to develop a mathematical model in which customers and service providers are considered as perspectives. The objectives of the developed model are minimization of the total cost of server provider and minimization of the total time of customers. This model has different real world applications, including locating bank automated teller machines (ATMs), different types of vendor machines, etc. For solving the model, two popular multi-objective evolutionary algorithms (MOEA) of the literature are implemented. The first algorithm is non-dominated sorted genetic algorithm (NSGA-II) and the second one is non-dominated ranked genetic algorithm (NRGA). Moreover, to illustrate the effectiveness of the proposed algorithms, some numerical examples are presented and analyzed statistically. The results indicate that the proposed algorithms provide an effective means to solve the problems. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Queuing Location-allocation Immobile servers multi-objective evolutionary algorithms Statistical tests

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Scheduling the English Football League with a multi-objective evolutionary Algorithm

Scheduling the English Football League with a Multi-objectiv...

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13th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： While, Lyndon Kendall, Graham Univ Western Australia Comp Sci & Software Engn Nedlands WA 6009 Australia Univ Nottingham Sch Comp Sci ASAP Res Grp Semenyih Selangor Malaysia Univ Nottingham Nottingham NG7 2RD England

ISBN: (纸本)9783319107622;9783319107615

We describe a multi-objective evolutionary algorithm that derives schedules for the English Football League over the busy New Year period according to seven objectives. The two principal objectives are to minimise travel distances for teams and supporters, and to minimise so-called "pair clashes" where teams which are geographically close play at home simultaneously, which can cause problems for police, and other logistical issues. The other five objectives implement various problem constraints. The schedules derived are often superior both to those used in the relevant years, and to those previously published in the literature, especially for the harder problem instances. In addition, the system returns a set of schedules offering different trade-offs between the main objectives, any of which might be of interest to the authorities.

关键词： Sports scheduling multi-objective evolutionary algorithms

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A Feature-Driven Crossover Operator for Product Line Architecture Design Optimization 38

A Feature-Driven Crossover Operator for Product Line Archite...

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IEEE 38th Annual International Computers, Software and Applications Conference (COMPSAC)

作者： Colanzi, Thelma Elita Vergilio, Silvia Regina Univ Fed Parana Dept Comp Sci BR-19031970 Curitiba Parana Brazil Univ Estadual Maringa Dept Comp Sci BR-87020900 Maringa Parana Brazil

ISBN: (纸本)9781479935741

The Product Line Architecture (PLA) design is a multi-objective optimization problem that can be properly solved in the Search Based Software Engineering (SBSE) field. However, the PLA design has specific characteristics. For example, the PLA is designed in terms of features and a highly modular PLA is necessary to enable the growth of a software product line. However, existing search based design approaches do not consider such needs. To overcome this limitation, this paper introduces a feature-driven crossover operator that aims at improving feature modularization. The proposed operator was applied in an empirical study using the multi-objective evolutionary algorithm named NSGAII. In comparison with another version of NSGAII that uses only mutation operators, the feature-driven crossover version found a greater diversity of solutions (potential PLA designs), with higher feature-based cohesion, and less feature scattering and tangling.

关键词： Product Line Architecture Design multi-objective evolutionary algorithms Crossover Operator

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NSGA-II: Implementation and Performance Metrics Extraction for CPU and GPU 16

NSGA-II: Implementation and Performance Metrics Extraction f...

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16th International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Padurariu, Florina Roxana Marinescu, Cristina Politehn Univ Timisoara Timisoara Romania West Univ Timisoara Timisoara Romania

ISBN: (纸本)9781479984480

multi-objective Optimization evolutionary algorithms are widely employed for solving different real-world optimization problems. Usually their runs involve a considerable amount of time because of the need to evaluate many functions. This particularity makes them good candidates of parallelization. In this work we investigate the benefits of the GPU implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) versus its CPU implementation in terms of the execution time.

关键词： multi-objective evolutionary algorithms GPU CPU empirical software engineering

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