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A NOVEL multi-objective ESTIMATION OF DISTRIBUTION ALGORITHM BASED ON SENSITIVITY OF objective FUNCTION

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第6期9卷 2543-2566页

作者： Seo, Hyunshik Lee, Chaewoo Ajou Univ Dept Elect & Comp Engn San 5 Suwon 443749 South Korea

Most multi-objective optimization problems (MOPs) have a set of optimal trade-off solutions known as the Pareto-optimal solutions since the objectives in MOPs are usually in conflict with one another. Recently proposed estimation of distribution algorithms (EDAs) build a probability distribution model based on the probabilistic information about decision variables of solutions, and then produce new solutions from the model. In the algorithms, the modeling technique enables the initial large search space to be reduced to small promising solution space during the search. However, the existing EDAs might be inefficient at generating the promising solutions since they depend on the information extracted from the decision variables of current solutions expected to approach the optimal solutions. For effective modeling of the promising solutions, we firstly introduce new information about the relationship between decision variables and objective functions;this information is called sensitivity of objective function. Secondly, we propose a multi-objective estimation of distribution algorithm based on the sensitivity of objective function (MOEDA-S). In the MOEDA-S, the sensitivity-based distribution modeling adapts to the current search strategy such that the convergence-focused search at the beginning part of the search is changed to a diversity-focused search at the latter part of the search. MOEDA-S is compared with two other leading multi-objective evolutionary algorithms on a set of test instances. The simulation results show that MOEDA-S outperforms the two compared algorithms in terms of both convergence and diversity performances of the solutions.

关键词： multi-objective optimization problems multi-objective evolutionary algorithms Estimation of distribution algorithms Variables sensitivity-based modeling

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Fuzzy Classification with multi-objective evolutionary algorithms

Fuzzy Classification with Multi-objective Evolutionary Algor...

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3rd International Workshop on Hybrid Artificial Intelligence Systems

作者： Jimenez, Fernando Sanchez, Gracia Sanchez, Jose F. Alcaraz, Jose M. Fac Informat Campus Espinardo Murcia 30071 Spain

ISBN: (纸本)9783540876557

In this work we propose, on the one hand, a multi-objective constrained optimization model to obtain fuzzy models for classification considering criteria of accuracy and interpretability. On the other hand, we propose an evolutionary multi-objective approach for fuzzy classification from data with real and discrete attributes. The multi-objective evolutionary approach has been evaluated by means of three different evolutionary schemes: Preselection with niches, NSGA-II and ENORA. The results have been compared in terms of effectiveness by means of statistical techniques using the well-known standard Iris data set.

关键词： multi-objective evolutionary algorithms Fuzzy classification

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Scheduling a Bi-objective Hybrid Flow Shop with Sequence-Dependent Family Setup Times Using Metaheuristics

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2013年第8期38卷 2233-2244页

作者： Fadaei, M. Zandieh, M. Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Shahid Beheshti Univ Management & Accounting Fac Dept Ind Management GC Tehran Iran

This paper deals with the problem of hybrid flow shop scheduling. In this investigation, we considered group scheduling within the area of sequence-dependent family setup times and two objectives of minimizing makespan and total tardiness are taken into consideration simultaneously. Due to the computational complexity in solving these set of problems with multiple objectives, metaheuristics has a high priority, because these algorithms are capable of solving combinatorial problems in a reasonable time. This study focuses on three multi-objective algorithms, multi-objective genetic algorithm, sub-population genetic algorithm-II and non-dominated sorting genetic algorithm-II, to solve the mentioned problem. In order to investigate the effectiveness and efficiency of applying the noted metaheuristics for such an NP-hard problem, we evaluate non-dominated solution sets obtained via each algorithm through some evaluation metrics.

关键词： multi-objective optimization Hybrid flow shop multi-objective evolutionary algorithms Sequence-dependent family setup times Group scheduling Makespan Total tardiness

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Dynamic Task Graph Scheduling on multicore Processors for Performance, Energy, and Temperature Optimization

Dynamic Task Graph Scheduling on Multicore Processors for Pe...

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International Green Computing Conference (IGCC)

作者： Sheikh, Hafiz Fahad Ahmad, Ishfaq Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

ISBN: (纸本)9781479906239

Despite significant advancements in multicore processor technology for reducing the chip-level energy consumption, higher levels of power dissipation resulting in thermal implications and cooling costs remain as unsolved problems. Although several scheduling methods of controlling and managing the power dissipation and temperature exist, most schemes are static that are unable to adjust to the dynamic program and system changes. This paper presents dynamic method for voltage-scaling based task scheduling for simultaneous optimization of performance, energy, and temperature (PET quantities) under dynamically varying task and system conditions. Our method generates an initial set of Pareto optimal solutions utilizing a multi-objective evolutionary algorithm (MOEA) called SPEA-II (Strength Pareto evolutionary Algorithm). This set of solutions is dynamically evolved with time to minimize the deviation of PET quantities from the Pareto optimal values. We carried out extensive evaluations using several task graph benchmarks based on the data obtained from a real multicore machine. The results indicate that the proposed dynamic re-optimization achieves up to 8% improvement in PET quantities as compared to the statically selected schedule.

关键词： dynamic task scheduling dynamic thermal management multi-objective evolutionary algorithms multicore system dynamic voltage and frequency scaling

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Determining Integration and Test Orders in the Presence of Modularization Restrictions

Determining Integration and Test Orders in the Presence of M...

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27th Brazilian Symposium on Software Engineering (SBES)

作者： Guez Assuncao, Wesley Klewerton Colanzi, Thelma Elita Vergilio, Silvia Regina Pozo, Aurora Fed Univ Parana UFPR DINF BR-81531980 Curitiba Parana Brazil Technol Fed Univ Parana UTFPR COINF BR-85902490 Toledo Brazil State Univ Maringa UEM DIN Comp Sci Dept BR-87020900 Maringa Parana Brazil

ISBN: (纸本)9780769551654

The Integration and Test Order problem is very known in the software testing area. It is related to the determination of a test order of modules that minimizes stub creation effort, and consequently testing costs. A solution approach based on multi-objective and evolutionary algorithms (MOEAs) achieved promising results, since these algorithms allow the use of different factors and measures that can affect the stubbing process, such as number of attributes and operations to be simulated by the stub. However, works based on such approach do not consider different modularization restrictions related to the software development environment. For example, the fact that some modules can be grouped into clusters to be developed and tested by independent teams. This is a very common practice in most organizations, particularly in that ones that adopt a distributed development process. Considering this fact, this paper introduces an evolutionary and multi-objective strategy to deal with such restrictions. The strategy was implemented and evaluated with real systems and three MOEAs. The results are analysed in order to compare the algorithms performance, and to better understand the problem in the presence of modularization restrictions. We observe an impact in the costs and a more complex search, when restrictions are considered. The obtained solutions are very useful and the strategy is applicable in practice.

关键词： Software testing multi-objective evolutionary algorithms distributed development

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Revisiting the NSGA-II Crowding-Distance Computation 13

Revisiting the NSGA-II Crowding-Distance Computation

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Fortin, Felix-Antoine Parizeau, Marc Univ Laval Dept Genie Elect & Genie Informat Lab Vis & Syst Numer Quebec City PQ G1V 0A6 Canada

ISBN: (纸本)9781450319638

This paper improves upon the reference NSGA-II procedure by removing an instability in its crowding distance operator. This instability stems from the cases where two or more individuals on a Pareto front share identical fitnesses. In those cases, the instability causes their crowding distance to either become null, or to depend on the individual's position within the Pareto front sequence. Experiments conducted on nine different benchmark problems show that, by computing the crowding distance on unique fitnesses instead of individuals, both the convergence and diversity of NSGA-II can be significantly improved.

关键词： multi-objective evolutionary algorithms NSGA-II crowding distance

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Software Effort Estimation as a multiobjective Learning Problem

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2013年第4期22卷 35-35页

作者： Minku, Leandro L. Yao, Xin Univ Birmingham Ctr Excellence Res Computat Intelligence & Applic Sch Comp Sci Birmingham B15 2TT W Midlands England

Ensembles of learning machines are promising for software effort estimation (SEE), but need to be tailored for this task to have their potential exploited. A key issue when creating ensembles is to produce diverse and accurate base models. Depending on how differently different performance measures behave for SEE, they could be used as a natural way of creating SEE ensembles. We propose to view SEE model creation as a multiobjective learning problem. A multiobjective evolutionary algorithm (MOEA) is used to better understand the tradeoff among different performance-measures by creating SEE models through the simultaneous optimisation of these measures. We show that the performance measures behave very differently, presenting sometimes even opposite trends. They are then used as a source of diversity for creating SEE ensembles. A good tradeoff among different measures can be obtained by using an ensemble of MOEA solutions. This ensemble performs similarly or better than a model that does not consider these measures explicitly. Besides, MOEA is also flexible, allowing emphasis of a particular measure if desired. In conclusion, MOEA can be used to better understand the relationship among performance measures and has shown to be very effective in creating SEE models.

关键词： Experimentation algorithms Management Software effort estimation ensembles of learning machines multi-objective evolutionary algorithms

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multi-objective evolutionary algorithm for donor-recipient decision system in liver transplants

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第2期222卷 317-327页

作者： Cruz-Ramirez, Manuel Hervas-Martinez, Cesar Carlos Fernandez, Juan Briceno, Javier de la Mata, Manuel Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain Hosp Reina Sofia CIBERehd Liver Transplantat Unit Cordoba Spain

This paper reports on a decision support system for assigning a liver from a donor to a recipient on a waiting-list that maximises the probability of belonging to the survival graft class after a year of transplant and/or minimises the probability of belonging to the non-survival graft class in a two objective framework. This is done with two models of neural networks for classification obtained from the Pareto front built by a multi-objective evolutionary algorithm - called MPENSGA2. This type of neural network is a new model of the generalised radial basis functions for obtaining optimal values in C (Correctly Classified Rate) and MS (Minimum Sensitivity) in the classifier, and is compared to other competitive classifiers. The decision support system has been proposed using, as simply as possible, those models which lead to making the correct decision about receptor choice based on efficient and impartial criteria. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks Generalised radial basis function Liver transplantation multi-objective evolutionary algorithms Organ allocations Rule-based system

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A multi-objective evolutionary algorithm for an effective tuning of fuzzy logic controllers in heating, ventilating and air conditioning systems

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APPLIED INTELLIGENCE 2012年第2期36卷 330-347页

作者： Jose Gacto, Maria Alcala, Rafael Herrera, Francisco Univ Jaen Dept Comp Sci Intelligent Syst & Data Min Res Grp Jaen Spain Univ Granada Dept Comp Sci & Artificial Intelligence Soft Comp & Intelligent Informat Syst Res Grp Granada Spain

This paper focuses on the use of multi-objective evolutionary algorithms to develop smartly tuned fuzzy logic controllers dedicated to the control of heating, ventilating and air conditioning systems, energy performance, stability and indoor comfort requirements. This problem presents some specific restrictions that make it very particular and complex because of the large time requirements needed to consider multiple criteria (which enlarge the solution search space) and the long computation time models required in each evaluation. In this work, a specific multi-objective evolutionary algorithm is proposed to obtain more compact fuzzy logic controllers as a way of finding the best combination of rules, thus improving the system performance to better solve the HVAC system control problem. This method combines lateral tuning of the linguistic variables with rule selection. To this end, two objectives have been considered, maximizing the performance of the system and minimizing the number of rules obtained. This algorithm is based on the well-known SPEA2 but uses different mechanisms for guiding the search towards the desired Pareto zone. Moreover, the method implements some advanced concepts such as incest prevention, that help to improve the exploration/exploitation trade-off and consequently its convergence ability. The proposed method is compared to the most representative mono-objective steady-state genetic algorithms previously applied to the HVAC system control problem, and to generational and steady-state versions of the most interesting multi-objective evolutionary algorithms (never applied to this problem) showing that the solutions obtained by this new approach dominate those obtained by these methods. The results obtained confirm the effectiveness of our approach compared with the rest of the analyzed methods, obtaining more accurate fuzzy logic controllers with simpler models.

关键词： Heating, ventilating, and air conditioning systems HVAC systems Fuzzy logic controllers Genetic tuning Linguistic 2-tuples representation Rule selection multi-objective evolutionary algorithms

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Formulation of the evolutionary-Based Data Assimilation, and its Implementation in Hydrological Forecasting

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WATER RESOURCES MANAGEMENT 2012年第13期26卷 3853-3870页

作者： Dumedah, Gift Monash Univ Dept Civil Engn Melbourne Vic 3800 Australia

multi-objective evolutionary algorithms (MOEAs) have gained popularity for their capability to handle complex and nonlinear problems. MOEAs are population-based search tools which employ the concept of biological evolution and natural selection. While MOEAs have been applied in numerous hydrological studies for parameter estimation, their formulation for solving data assimilation (DA) problems has not been completely formalized in the literature. This study presents the evolutionary-based data assimilation (EDA) where it formulates the MOEA strategy into an applied DA procedure. The study outlines the stochastic and adaptive capabilities of MOEAs, and shows how MOEA operators including Pareto dominance, crossover, and random variation are naturally suited to handle DA problems. The EDA employs the cost function from variational DA to approximate the least squares estimate between ensemble simulations and perturbed observation. The EDA uses the MOEA strategy to evolve a population of competing members through several cycles of evolution at each assimilation step. The EDA determines several non-dominated members for each assimilation time step, allows these members to evolve, and evaluate updated members for subsequent time steps. Several ensemble members are evaluated for each assimilation time step but the updated ensembles are determined as a subset of the final evolved population which comprise the Pareto-optimal set. The EDA has been illustrated in a practical implementation to assimilate daily streamflow into the Sacramento Soil Moisture Accounting model.

关键词： multi-objective evolutionary algorithms Model-data integration Pareto-optimality

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