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作者： HAMID REZA KHOSRAVANI UNIVERSIDADE DO ALGARVE

学位级别：博士

Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings have the biggest proportion in energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. Hence this PhD was intended towards managing the energy consumed by Heating, Ventilating and Air Conditioning (HVAC) systems in buildings benefiting from Model Predictive Control (MPC) technique. To achieve this goal, artificial intelligence models such as neural networks and Support Vector Machines (SVM) have been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not noise-free. In this PhD, Radial Basis Function Neural Networks (RBFNN) as a promising class of Artificial Neural Networks (ANN) were considered to model a sequence of time series processes where the RBFNN models were built using multi objective genetic algorithm (MOGA) as a design platform. Regarding the design of such models, two main challenges were tackled; data selection and model adaptation. Since RBFNNs are data driven models, the performance of such models relies, to a good extent, on selecting proper data throughout the design phase, covering the whole input-output range in which they will be employed. The convex hull algorithms can be applied as methods for data selection; however the use of conventional implementations of these methods in high dimensions, due to their high complexity, is not feasible. As the first phase of this PhD, a new randomized approximation convex hull algorithm called ApproxHull was proposed for high dimensions so that it can be used in an acceptable execution time, and with low memory requirements. Simulation results showed that applying ApproxHull as a filter data selection method (i. e., unsupervised data selection method) could improve the performance of the class

关键词： Neural Networks multi-objective genetic algorithm Data Selection Online Adaptation

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On Performance Improvement Based on Restart Meta-Heuristic Implementation for Solving multi-objective Optimization Problems 8th

On Performance Improvement Based on Restart Meta-Heuristic I...

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8th International Conference on Swarm Intelligence (ICSI)

作者： Brester, Christina Ryzhikov, Ivan Semenkin, Eugene Siberian State Aerosp Univ Krasnoyarsk Russia

ISBN: (纸本)9783319618333;9783319618326

One of the possible goals of multi-objective optimization is finding a set of non-dominated solutions or, in other words, a Pareto set approximation. Population-based algorithms, in particular, genetic algorithms, are widely used for this purpose because they deal with a set of alternative solutions, which might be helpful when a number of trade-off points should be obtained. To get a representative approximation, various regions of a search space should be explored. However, during the algorithm execution a search might be stuck in some areas. Therefore, in this article we present a new restart operator for multi-objective genetic algorithms which can prevent a search from stagnating, help to explore new regions and, as a result, improve the algorithm performance significantly. In our proposal we answer the two crucial questions of a restarting concept which are when to restart an algorithm and how to use previously found solutions. We introduce the algorithm independent restart operator, even though in this work we investigate it in combination with a certain MOGA. The experimental results prove the high effectiveness of the modified MOGA with the incorporated restart operator in comparison with the conventional one.

关键词： multi-objective genetic algorithm Restart operator Performance Benchmark problems

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Solving and optimizing a bi-objective open shop scheduling problem by a modified genetic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第5-8期85卷 1603-1613页

作者： Azadeh, Ali Goldansaz, SeyedMorteza Zahedi-Anaraki, AmirHossein Univ Tehran Sch Ind & Syst Engn Ctr Excellence Intelligent Based Expt Mech Coll EngnDept Engn Optimizat Res Tehran Iran

This paper presents a new bi-objective mixed-integer linear programming model for open shop scheduling problem. The main constraints of this model are considering independent setup times and sequence-dependent transportation times. Two objectives are presented, minimizing sum of the makespan for all jobs and minimizing sum of the machines' workload. Because of the complexity of this problem, it is known as a NP-hard problem;so, it cannot obtain an optimal solution in a reasonable time by using traditional methods. According to classic approach, some small- and medium- to large-sized instances are generated. For the small-sized ones, the Pareto-optimal solutions are produced exactly, and for the medium- to large-sized instances, we proposed a non-dominated sorting genetic algorithm (NSGA-II). To evaluate the performance of the proposed NSGA-II, the augmented epsilon-constraint method is used for the small-sized instances, and three performance metrics are proposed for the medium- to large-sized instances.

关键词： Bi-objective open shop scheduling Makespan Machine workload multi-objective genetic algorithm Optimization

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Optimal Combinations of Non-Sequential Regressors for ARX-Based Typhoon Inundation Forecast Models Considering multiple objectives

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WATER 2017年第7期9卷 519-519页

作者： Ouyang, Huei-Tau Shih, Shang-Shu Wu, Ching-Sen Natl Ilan Univ Dept Civil Engn Yilan 26047 Taiwan Natl Taiwan Univ Dept Civil Engn Taipei 10617 Taiwan

Inundation forecast models with non-sequential regressors are advantageous in efficiency due to their rather fewer input variables required to be processed. This type of model is nevertheless rare mainly because of the difficulty in finding the proper combination of regressors for the model to perform accurate prediction. A novel methodology is proposed in this study to tackle the problem. The approach involves integrating a multi-objective genetic algorithm (MOGA) with forecast models based on ARX (Auto-Regressive model with eXogenous inputs) to transfer the search for the optimal combination of non-sequential regressors into an optimization problem. An innovative approach to codifying any combinations of model regressors into binary strings is developed and employed in MOGA. The Pareto optimal sets of three types of models including linear ARX (LARX), nonlinear ARX with Wavelet function (NLARX-W), and nonlinear ARX with Sigmoid function (NLARX-S) are searched for by the proposed methodology. The results show that the optimal models acquired through this approach have good inundation forecasting capabilities in every aspect in terms of accuracy, time shift error, and error distribution.

关键词： typhoon inundation ARX model non-sequential regressors multi-objective genetic algorithm

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Power Efficient Resource Allocation in Cloud Computing Data Centers using multi-objective genetic algorithms and Simulated Annealing 4

Power Efficient Resource Allocation in Cloud Computing Data ...

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4th IEEE International Conference on Cloud Networking (CloudNet)

作者： Portaluri, Giuseppe Giordano, Stefano Univ Pisa I-56100 Pisa Italy

ISBN: (纸本)9781467395014

In this paper we present a new task allocator for Cloud Data Center (DC). The implementation is based on two different heuristics: multi-objective genetic algorithms (MOGA) and Simulated Annealing (SA). The allocator reduces at the same time both task completion time and server and switches power consumption, avoiding network link congestion. The evaluation results show that the developed approach is able to perform the static allocation of a large number of independent tasks on homogeneous single-core servers with a quadratic time complexity for MOGA and a linear time complexity for SA.

关键词： Data center Cloud Computing multi-objective genetic algorithm Simulated Annealing Resource Allocation Power Efficiency

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Capacity planning in thin film transistor - Liquid crystal display cell process

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JOURNAL OF MANUFACTURING SYSTEMS 2016年 39卷 63-78页

作者： Chen, James C. Chen, Tzu-Li Pratama, Bayu Rezki Tu, Qian-Fang Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan Fu Jen Catholic Univ Dept Informat Management New Taipei Taiwan Natl Taiwan Univ Sci & Technol Dept Ind Management New Taipei Taiwan

The cell process in a thin film transistor-liquid crystal display (TFT-LCD) manufacturing is characterized as a complicated multi-stage and parallel-machine production system. This system has multiple products, sequence-dependent setup times, multi-step production, multi-objective, and matching constraints between color filter lines and array lines. This complex and capital-intensive production environment needs an efficient capacity planning decision that determines the order release schedule, estimated machine start and finished time of each order, and machine allocations to improve the performance of the entire production process. This research first proposes a finite capacity planning system (FCPS) to cope with the capacity planning problem faced by the TFT-LCD cell process based on the pull philosophy and assumption of finite capacity. A modified multi-objective genetic algorithm (MOGA) embedded in FCPS is also developed to efficiently generate the order release decision, which simultaneously minimizes the machine workload balance, makespan, and lateness. The preliminary experiment identifies the fine MOGA tuning parameters using a two-level factorial experimental design. Finally, the full factorial experiment evaluates the significance and robustness of the proposed modified MOGA algorithm compared with other competitive algorithms under various circumstances. (C) 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Capacity planning TFT-LCD Cell process Finite capacity multi-objective genetic algorithm

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Interior High Frequency Noise Analysis of Heavy Vehicle Cab and multi-objective Optimization with Statistical Energy Analysis Method

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FLUCTUATION AND NOISE LETTERS 2017年第2期16卷 1750017-1750017页

作者： Chen, Shuming Wang, Lianhui Song, Jiqang Wang, Dengfeng Chen, Jing Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China FAW CAR CO LTD Changchun 130011 Peoples R China

The interior sound pressure levels of a commercial vehicle cab at the driver's right ear position and head rest position are determined as evaluation indices of vehicle acoustic performances. A statistical energy analysis model of the commercial vehicle cab was created by using statistical energy analysis method. The simulated interior acoustic performance of the cab has a significant coincidence with the experimental results. A response surface model was presented to determine the relationship between sound package parameters and evaluation indices of the interior acoustic performance for the vehicle cab. A multi-objective optimization was performed by using NSGA II algorithm with weighting coefficient method. The presented method provides a new idea for the multi-objective optimization design of the acoustic performances in vehicle noise analysis and control field.

关键词： Statistical energy analysis vehicle cab high frequency noise response surface method multi-objective genetic algorithm

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A feature-driven crossover operator for multi-objective and evolutionary optimization of product line architectures

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JOURNAL OF SYSTEMS AND SOFTWARE 2016年 121卷 126-143页

作者： Colanzi, Thelma Elita Vergilio, Silvia Regina State Univ Maringa UEM Informat Dept DIN BR-87020900 Maringa Parana Brazil Fed Univ Parana UFPR Comp Sci Dept DInf BR-81531970 Curitiba Parana Brazil

The optimization of a Product Line Architecture (PLA) design can be modeled as a multi-objective problem, influenced by many factors, such as feature modularization, extensibility and other design principles. Due to this it has been properly solved in the Search Based Software Engineering (SBSE) field. However, previous empirical studies optimized PLA design using the multi-objective and evolutionary algorithm NSGA-II, without applying one of the most important genetic operators: the crossover. To overcome this limitation, this paper presents a feature-driven crossover operator that aims at improving feature modularization in PLA design. The proposed operator was applied in two empirical studies using NSGA-II in comparison with another version of NSGA-II that uses only mutation operators. The results show the usefulness and applicability of the proposed operator. The NSGA-II version that applies the feature-driven crossover found a greater diversity of solutions (potential PLA designs), with higher feature-based cohesion, and less feature scattering and tangling. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Product line architecture design multi-objective genetic algorithm Crossover operator Empirical study

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Vibration control of a rotor supported by journal bearings and an asymmetric high-static low-dynamic stiffness suspension

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NONLINEAR DYNAMICS 2016年第1期85卷 525-545页

作者： Abbasi, Amirhassan Khadem, S. E. Bab, Saeed Friswell, M. I. Tarbiat Modares Univ Dept Mech Engn POB 14115-177 Tehran Iran Swansea Univ Coll Engn Bay CampusFabian Way Swansea SA1 8EN W Glam Wales

In this paper, the optimum design of an asymmetric model of a high-static low-dynamic stiffness (HSLDS) suspension for the vibration control of a rotating machine is studied. The rotating system consists of a flexible rotor supported by short journal bearings and HSLDS suspensions and is excited by an unbalance force. The equations of motion are solved numerically using the Runge-Kutta method. The bifurcation diagrams, phase portraits, Poincare maps and power spectra show the existence of various response regimes, such as periodic, period-two and quasi-periodic, in the dynamic response of the system. The design procedure is formulated as a multi-objective optimization problem which minimizes objectives including the rotor and bearing vibration, the bearing force transmission and the compliance of the HSLDS suspension under static loading, to obtain the appropriate force-displacement relation for the asymmetricHSLDS suspension. The multi-objective genetic algorithm is applied to identify the best set of the design parameters. The advantages of using an optimum HSLDS suspension to fulfill the design requirements in an operating speed range are demonstrated. It is also shown that the optimum suspension with low-dynamic stiffness can suppress the non-periodic behavior of the rotating machine in the considered operating speed range.

关键词： High-static low-dynamic stiffness Rotor Short journal bearing Bifurcation Phase portraits Poincare maps Power spectra Periodic Period-two Quasi-periodic multi-objective genetic algorithm

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A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly

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JOURNAL OF MANUFACTURING SYSTEMS 2016年 41卷 239-255页

作者： Jiang, Hui Yi, Jianjun Chen, Shaoli Zhu, Xiaomin East China Univ Sci & Technol Dept Mech Engn 130 Meilong Rd Shanghai 200237 Peoples R China

Some manufacturers outsource their disassembly tasks to professional factories, each factory of them has specialized in its disassembly ability. Different disassembly facilities are usually combined to execute disassembly tasks. This study proposes the cloud-based disassembly that abstracts ability of the disassembly factory as the disassembly resource, the disassembly resource is then able to be allocated to execute disassembly tasks. Based on this concept, the cloud-based disassembly system is proposed, which provides the disassembly service according to the user requirement. The disassembly service is the execution plan for disassembly tasks, which is the result of scheduling disassembly tasks and allocating disassembly resources. To formally describe the disassembly service, this paper builds a mathematical model that considers the uncertainty nature of the disassembly process and precedence relationships of disassembly tasks. Two objectives including minimizing the expected total makespan and minimizing the expected total cost of the disassembly service are also discussed. The mathematical model is NP complete, a multi-objective genetic algorithm based on non-dominated sorting genetic algorithm II is designed to address the problem. Computation results show that the proposed algorithm performs well, the algorithm generates a set of Pareto optimal solutions. The user can choose a preferred disassembly service among Pareto optimal solutions. (C) 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Cloud-based disassembly multi-objective genetic algorithm Task scheduling and resource allocation Cloud manufacturing

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