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A Smart-Distributed Pareto Front Using the ev-MO GA evolutionary Algorithm

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2014年第2期23卷 1450002-1450002页

作者： Herrero, J. M. Reynoso-Meza, G. Martinez, M. Blasco, X. Sanchis, J. Univ Politecn Valencia Predict Control & Heurist Optimizat Grp Dept Syst Engn & Control Valencia 46022 Spain

Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the normalized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC optimization method presents several disadvantages related with the procedure itself, initial condition dependency, and computational burden. In this article, the epsilon-variable multi-objective genetic algorithm (ev-MOCA) is presented. This algorithm characterizes the Pareto front in a smart way and removes the disadvantages of the NNC method. Finally, examples of a three-bar truss design and controller tuning optimizations are presented for comparison purposes.

关键词： multi-objective optimization Pareto front engineering design evolutionary algorithms multi-objective evolutionary algorithms

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A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2013年第2期24卷 277-293页

作者： Cheshmehgaz, Hossein Rajabalipour Desa, Mohamad Ishak Wibowo, Antoni UTM Fac Comp Sci & Informat Syst Skudai 81310 Johor Malaysia

Nowadays, time and cost are familiar criteria for every logistic provider, and both have been long treated to be minimized simultaneously. However, the criteria are naturally conflicted even with flexibilities and/or constraints appeared in the logistic networks. This paper is concerned with three-level logistic networks with potential suppliers, distributed centers (DCs), and deterministic demands from available consumers. The networks also benefit from potential direct shipments from suppliers to consumers as long as suppliers and DCs facilities might face limited capacity in their own seasonal supplying and warehousing processes. The goal is (re)configure the networks in order to minimize response time to consumers, transportation cost and facility cost. Therefore, the networks are formulated as multiple criteria decision making problems, which have more than one configuration through the time and cost optimizing at the same time. Due to the flexibility and the constraints, the decision maker(s) needs a set of compromise solutions for the networks that represent optimal configurations based on the objectives without considering prior knowledge. To this end, the problems are formulated into four individual logistic network models varying with the flexibility option and/or the capacitated facilities. To find the compromise solutions, Pareto-based multi-objective evolutionary algorithm, NSGA-II is customized and then utilized to deal with an illustrative case study. The results are analyzed through the two performance measures, hypervolume and the number of optimal solutions obtained so far.

关键词： Flexible logistic network model multi-objective optimization Pareto solutions multi-objective evolutionary algorithms

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Neural Network based HVAC Predictive Control

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IFAC Proceedings Volumes 2014年第3期47卷 3617-3622页

作者： António E Ruano Pedro M. Ferreira Centre for Intelligent Systems IDMEC IST and University of Algarve 8005-139 Faro Portugal University of Lisbon Faculty of Sciences Large-scale Informatics Systems Lab. (LaSIGE) Portugal

This paper addresses the problem of controlling Heating Ventilation and Air Conditioning (HVAC) systems with the purpose of maintaining a desired thermal comfort level, whilst minimizing the electrical energy required. Using a pilot installation, in the University of Algarve, Portugal, a Model Based Predictive Control (MBPC) strategy is used to control the HVAC equipment. The thermal comfort is assessed using the predicted mean vote (PMV) index. The MBPC methodology uses predictive models, implemented by radial basis function neural networks, identified by means of a multi-objective Genetic Algorithm (MOGA). Experimental results show that this approach is feasible and robust, and able to obtain energy savings greater than 50%, under normal building occupation.

关键词： HVAC Predictive Control Predicted Mean Vote Neural Networks multi-objective evolutionary algorithms Wireless Sensor Networks

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Project scheduling: A multi-objective evolutionary algorithm that optimizes the effectiveness of human resources and the project makespan

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ENGINEERING OPTIMIZATION 2013年第1期45卷 45-65页

作者： Yannibelli, Virginia Amandi, Analia UNCPBA Fac Cs Exactas ISISTAN Res Inst Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn CONICET Buenos Aires DF Argentina

In this article, the project scheduling problem is addressed in order to assist project managers at the early stage of scheduling. Thus, as part of the problem, two priority optimization objectives for managers at that stage are considered. One of these objectives is to assign the most effective set of human resources to each project activity. The effectiveness of a human resource is considered to depend on its work context. The other objective is to minimize the project makespan. To solve the problem, a multi-objective evolutionary algorithm is proposed. This algorithm designs feasible schedules for a given project and evaluates the designed schedules in relation to each objective. The algorithm generates an approximation to the Pareto set as a solution to the problem. The computational experiments carried out on nine different instance sets are reported.

关键词： project scheduling human resource assignment multi-skilled resources effectiveness of human resources multi-objective evolutionary algorithms

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A new methodology for calculating roadway lighting design based on a multi-objective evolutionary algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第6期40卷 2156-2164页

作者： Gomez-Lorente, Daniel Rabaza, O. Espin Estrella, A. Pena-Garcia, A. Univ Granada Dept Civil Engn E-18071 Granada Spain

This paper presents a new method for calculating the design of roadway lighting. Apart from its accuracy, this method, which is based on a multi-objective evolutionary algorithm, has the added advantage of enhancing the energy efficiency of lighting installations. This is positive because the economic use of energy resources is evidently a priority in the world today. In our study, an exhaustive calibration process was used to fine-tune the accuracy and precision of the new method presented. The results obtained were then compared with those of DIALUX, a well-known free software program that is frequently used for the design of lighting installations. In the second phase of this research, the lighting installation was made more complex in order to verify the applicability of this new method to a wide range of different contexts. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithms Numerical optimization Roadway lighting Installation efficiency

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A multi-objective evolutionary Algorithm for enhancing Bayesian Networks hybrid-based modeling

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2013年第10期66卷 1971-1980页

作者： Garcia-Alonso, Carlos R. Campoy-Munoz, Pilar Salazar Ordonez, Melania Univ LOYOLA Andalucia Dept Quantitat Methods Cordoba 14004 Spain Univ LOYOLA Andalucia Dept Econ Cordoba 14004 Spain

Bayesian Networks are increasingly being used to model complex socio-economic systems by expert knowledge elicitation even when data is scarce or does not exist. In this paper, a multi-objective evolutionary Algorithm (MOEA) is presented for assessing the parameters (input relevance/weights) of fuzzy dependence relationships in a Bayesian Network (BN). The MOEA was designed to include a hybrid model that combines Monte-Carlo simulation and fuzzy inference. The MOEA-based prototype assesses the input weights of fuzzy dependence relationships by learning from available output data. In socio-economic systems, the determination of how a specific input variable affects the expected results can be critical and it is still one of the most important challenges in Bayesian modeling. The MOEA was checked by estimating the migrant stock as a relevant variable in a BN model for forecasting remittances. For a specific year, results showed similar input weights than those given by economists but it is very computationally demanding. The proposed hybrid-approach is an efficient procedure to estimate output values in BN. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithms Fuzzy inference Bayesian networks Monte-Carlo simulation Remittances

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Preventive maintenance optimisation of multi-equipment manufacturing systems by combining discrete event simulation and multi-objective evolutionary algorithms

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PRODUCTION PLANNING & CONTROL 2008年第4期19卷 342-355页

作者： Oyarbide-Zubillaga, A. Goti, A. Sanchez, A. Univ Mondragon Ind Management Dept Arrasate Mondragon Spain Univ Politecn Valencia Dept Stat & Operat Res E-46071 Valencia Spain

This paper is focused on preventive maintenance optimisation in manufacturing environments, with the objective of determining the optimal preventive maintenance frequencies for multi-equipment systems under cost and profit criteria. The initiative considers the interaction of production, work in process material, quality and maintenance aspects. In this work the suitability of discrete event simulation to model or modify complex system models is combined with the aptitude that multi-objective evolutionary algorithms have shown to deal with multi-objective problems to develop a maintenance management and optimisation approach. An application case is performed where the activities applied on a system that produces hubcaps for the car making industry, showing the quantitative benefits of adopting the detailed approach.

关键词： preventive maintenance multi-equipment optimisation discrete event simulation multi-objective evolutionary algorithms metaheuristics

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A New Many-objective evolutionary Algorithm Based on Self-Adaptive Differential Evolution

A New Many-Objective Evolutionary Algorithm Based on Self-Ad...

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9th International Conference on Natural Computation (ICNC)

作者： Zhao, Hongyan Xiao, Jing Liaoning Prov Coll Commun Coll Informat Engn Shenyang Peoples R China

ISBN: (纸本)9781467347143

To improve the performance of the existing multi-objective evolutionary algorithms (MOEAs), we propose a new self-adaptive differential evolution algorithm for solving many-objective optimization problems (MOPs). To address the challenges in many-objective optimization, new selection strategy and density estimation method are designed to improve the performance of the elite MOEA model used by several exiting MOEAs. In addition, new mutation strategy and parameter adaptive method of DE are proposed to enhance the convergence ability of the evolution strategy utilized in MOEAs. Experimental results on ZDT and DTLZ test problems show that, the proposed algorithm, named SDEMO, is able to find much better spread of solutions with better approximating the true Pareto-optimal front compared to six state-of-the-art MOEAs.

关键词： multi-objective evolutionary algorithms many-objective optimization differential evolution elite selection strategy crowding density estimation

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Many-objective Optimization Using Taxi-Cab Surface evolutionary Algorithm

Many-Objective Optimization Using Taxi-Cab Surface Evolution...

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7th International Conference on evolutionary multi-Criterion Optimization (EMO)

作者： Moen, Hans J. F. Hansen, Nikolai B. Hovland, Harald Torresen, Jim Norwegian Def Res Estab Informat Management Div Oslo Norway Univ Oslo Dept Informat N-0316 Oslo Norway Univ Oslo Dept Math N-0316 Oslo Norway

ISBN: (纸本)9783642371400

Optimization of problems spanning more than three objectives, called many-objective optimization, is often hard to achieve using modern algorithm design and currently available computational resources. In this paper a multi-objective evolutionary algorithm, called the Surface evolutionary Algorithm, is extended into many-objective optimization by utilizing, for the first time, the taxi-cab metric in the optimizer. The Surface evolutionary Algorithm offers an alternative to multi-objective optimizers that rely on the principles of domination, hypervolume and so forth. The taxi-cab metric, or Manhattan distance, is introduced as the selection criterion and the basis for calculating attraction points in the Surface evolutionary Algorithm. This allows for fast and efficient many-objective optimization previously not attainable using this method. The Taxi-Cab Surface evolutionary Algorithm is evaluated on a set of well-known many-objective benchmark test problems. In problems of up to 20 dimensions, this new algorithm of low complexity is tested against several modern multi-objective evolutionary algorithms. The results reveal the Taxi-Cab Surface evolutionary Algorithm as a conceptually simple, yet highly efficient many-objective optimizer.

关键词： multi-objective optimization multi-objective evolutionary algorithms many-objective optimization

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Robust active shock control bump design optimisation using hybrid parallel MOGA

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COMPUTERS & FLUIDS 2013年第1期80卷 214-224页

作者： Lee, D. S. Bugeda, G. Periaux, J. Onate, E. CIMNE Barcelona 08034 Spain Univ Politecn Cataluna ES-08034 Barcelona Spain Deloitte Analyt Deloitte Consulting LLC Seoul South Korea

The paper investigates a robust optimisation for detail design of active shock control bump on a transonic Natural Laminar Flow (NLF) aerofoil using a multi-objective evolutionary Algorithm (MOEA) coupled to Computational Fluid Dynamics (CFDs) software. For MOEA, Robust multi-objective Optimisation Platform (RMOP) developed at CIMNE is used. For the active shock control bump design, two different optimisation methods are considered;the first method is a Pareto-Game based Genetic Algorithm in RMOP (denoted as RMOGA). The second method uses a Hybridised RMOGA with Game-Strategies and a parallel computation for high performance computation. Numerical results show not only how the concept of Shock Control Bump (SCB) coupled to CFD can improve aerodynamic performance of classic transonic aerofoil at the variability of flight conditions but also how high performance (parallel/distributed) computation with applying Hybrid-Game increases the efficiency of optimisation in terms of computational cost and results accuracy. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Robust/uncertainty design multi-objective evolutionary algorithms Game strategies Parallel computation Computational fluid dynamics Shock control bump

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