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4th IFAC Conference on Intelligent Control and Automation Sciences (ICONS)

作者： Ruano, A. Pesteh, S. Silva, S. Duarte, H. Mestre, G. Ferreira, P. M. Khosravani, H. Horta, R. Univ Algarve Fac Sci & Technol Faro Portugal Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal Univ Algarve Ctr Empresarial Gambelas EasySensing Intelligent Syst P-8005139 Faro Portugal Univ Lisbon Fac Ciencias LaSIGE P-1699 Lisbon Portugal Rolear SA P-8001906 Faro Portugal

This paper describes the application of a complete MBPC solution for existing HVAC systems, with a focus on the implementation of the objective function employed. Real-time results obtained with this solution, in terms of economical savings and thermal comfort, are compared with standard, temperature regulated control.(1) (C) 2016, IFAC (International Federation of Antomatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： HVAC Model-Based Predictive Control Predicted Mean Vote Neural Networks multi-objective genetic algorithm Weather Station Wireless Sensor Networks

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TIME-OPTIMAL PATH PLANNING AND CONTROL USING NEURAL NETWORKS AND A genetic algorithm

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International Journal of Computational Intelligence and Applications 2002年第2期2卷 153-172页

作者： NACHOL CHAIYARATANA ALI M. S. ZALZALA Research and Development Center for Intelligent Systems King Mongkut's Institute of Technology North Bangkok Thailand The IKMA Laboratory School of Engineering and Physical Sciences Heriot-Watt University Edinburgh EH14 4AS UK

This paper presents the use of neural networks and a genetic algorithm in time-optimal control of a closed-loop 3-dof robotic system. Extended Kohonen networks which contain an additional lattice of output neurons are used in conjunction with PID controllers in position control to minimize command tracking errors. The extended Kohonen networks are trained using reinforcement learning where the overall learning algorithm is derived from a self-organizing feature-mapping algorithm and a delta learning rule. The results indicate that the extended Kohonen network controller is more efficient than other techniques reported in early literature when the robot is operated under normal conditions. Subsequently, a multi-objective genetic algorithm (MOGA) is used to solve an optimization problem related to time-optimal control. This problem involves the selection of actuator torque limits and an end-effector path subject to time-optimality and tracking error constraints. Two chromosome coding schemes are explored in the investigation: Gray and integer-based coding schemes. The results suggest that the integer-based chromosome is more suitable at representing the decision variables. As a result of using both neural networks and a genetic algorithm in this application, an idea of a hybridization between a neural network and a genetic algorithm at the task level for use in a control system is also effectively demonstrated.

关键词： Kohonen network multi-objective genetic algorithm time-optimal control robotics

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multi-point robust design optimization of wind turbine airfoil under geometric uncertainty

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2012年第A2期226卷 245-261页

作者： Ju, Y. P. Zhang, C. H. Xi An Jiao Tong Univ Dept Fluid Machinery & Engn Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China

Modern wind turbine airfoil designs are increasingly emphasizing low sensitivity to the leading edge roughness in addition to good aerodynamic performances under varying wind conditions. In this study, a multi-point robust design optimization method has been systematically established for the wind turbine airfoil. The objective is to not only maximize the lift-to-drag ratio and lift coefficient at and near the design point, but also minimize their sensitivity to the leading edge roughness associated with the geometry profile uncertainty. The geometry parameterization of the airfoil is conducted by the Bezier curves. In the robust optimization, the multi-objective genetic algorithm, Monte Carlo simulation technique, and artificial neural network model are used. The results show that both the robust Pareto optimal (RPO) and deterministic Pareto optimal (DPO) airfoils have higher lift-to-drag ratio and lift coefficient than the original design FX 63-137 at and near the design point. A smaller maximum camber and larger radii near the leading edge help the RPO airfoil outperform both the DPO and original ones in terms of low sensitivity to the leading edge roughness. This study may be useful to the future development of the wind turbine airfoils with higher efficiency and reliability.

关键词： wind turbine airfoil leading edge roughness robust design optimization multi-objective genetic algorithm Monte Carlo simulation artificial neural network

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A genetic algorithm tool for conceptual structural design with cost and embodied carbon optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年 112卷 104711-104711页

作者： Kanyilmaz, Alper Tichell, Patricia Raquel Navarro Loiacono, Daniele Politecn Milan Dept Architecture Built Environm & Construct Engn Milan Italy Univ Politecn Valencia Dept Construct Engn & Civil Engn Projects Valencia Spain Politecn Milan Dept Elect Informat & Bioengn Milan Italy

The conceptual design decisions have the largest influence on a building project's safety, value, and environmental impact;hence they are commonly assigned to a "senior engineer'' to make use of his/her experience. However, the senior engineers can be biased towards solutions inside their area of expertise, which often prevents them from finding the best solutions among alternatives that must consider complex inter-related, and multi-disciplinary parameters. The engineering community could benefit from a rapid and high-quality decision-making method or tool to increase the speed and quality of its high-impact design choices. There are valuable studies in the literature exploiting Artificial Intelligence (AI) to improve the structural design process;however, most of them focus on the final design stage (e.g., Building Information Modeling), and the rest requires an existing project database (e.g., architectural drawings, already decided material types) to propose a small number of initial design alternatives. In this article, we present the development and validation of a genetic algorithm tool based on Non-dominated Sorted genetic algorithm II (NSGA-II) that can be used to analyse a wide range of safe, economical and low-CO2 options for the conceptual design of buildings. The design space starts from a design brief (with only the information about the site characteristics and project objectives). The solutions are explored with the material, grid size, floor type, lateral resistance, and foundation system variables. In a short computational time (< 2 min per run), users are provided with a Pareto graph of a large set of feasible solutions (in terms of cost, embodied CO2 emissions and free space) that an engineer would not be typically able to evaluate within a traditional conceptual design process. For future applications, the methodology presented in this paper is flexible to include more engineering materials (e.g., timber, masonry, structural glass), complex arch

关键词： Non-dominated sorted genetic algorithm genetic algorithm Conceptual structural design multi-objective genetic algorithm Design space exploration multi-criteria decision making Decision support Conceptual design Embodied carbon Life cycle analysis Cost-efficient structural design Preliminary structural

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Optimization of cooperative outsourcing between single-outsourcer multiple-subcontractors: a genetic algorithm based approach

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IFAC Proceedings Volumes 2012年第6期45卷 975-980页

作者： M. Haoues M. Dahane N.K. Mouss N. Rezg Automation and Manufacturing Laboratory (LAP) Industrial Engineering Department at University of Batna 1 Avenue Chahid Boukhlouf Med Ben Elhadi 05000 Batna - Algeria Industrial Engineering and Production Laboratory of Metz Engineer National School of Metz (ENIM) Route d'Ars Laquenexy CS65820 57078 - Metz. Metz Cedex 3 France Industrial Engineering and Production Laboratory of Metz Lorraine University – Metz (UdL) Île de Saulcy Metz Cedex 1 57045 France

In this paper, a cooperative outsourcing platform between single-outsourcer multiple-subcontractors has been developed under a combined approach of maintenance management and production control. This platform is composed of n independent subcontractor companies; each one has a single randomly failing and repairable machine. The basic objective is to develop mathematical models for sharing the outsourcing request between the subcontractors and to find the best outsourcing periods. Different multi-subcontractor strategies is adopted, namely, equitable distribution strategy, competitive strategy, the invoice outsourcing minimization strategy and multi-objective optimization strategy. For each model, we develop optimization algorithms and conduct computational experiments to study the managerial insights for the models and the algorithms.

关键词： cooperative outsourcing production control maintenance failure rate inventory genetic algorithm multi-objective genetic algorithm

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Hybrid Evolutionary Framework for Selection of Genes Predicting Breast Cancer Relapse

Hybrid Evolutionary Framework for Selection of Genes Predict...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Perino, Lorenzo Cascianelli, Silvia Masseroli, Marco Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9781728169262

Predicting relapse events is still one of the major challenges for breast cancer research. Despite gene expression-based classifiers may tackle this task, working on thousands of genes and only few samples jeopardizes the performances of a classifier trained without a proper gene selection. We propose a novel hybrid evolutionary gene selection framework, which uses a multi-objective genetic algorithm (MOGA) to search a wider range of gene selections and handles MOGA results in a whole new way, so as to overcome the limit of the non-easy interpretability of the MOGA broad set of solutions. To a classifier our framework provides a gene signature not only bringing the best cross-validation result, but also having noteworthy and robust performances when tested on unseen samples of an hold-out set. The robustness in hold-out showed the strength of our innovative key element: the final module of the framework, which fully exploits the high variability of MOGA outputs, rather than choosing just one of the solutions, as commonly done in the literature. It combines all MOGA results in more robust and compact gene occurrence-based signatures, under the reasonable assumption that highly recurrent genes have a more crucial biological role, more suitable clinical application and good discriminative power between relapsed and relapse-free patients, as confirmed by the obtained classification results.

关键词： Breast cancer relapse Gene selection Hybrid feature selection multi-objective genetic algorithm

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Large System multi-objective Model of Optimal Allocation for Water Resources in Jiansanjiang Branch Bureau

Large System Multi-objective Model of Optimal Allocation for...

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5th International Conference on Computer and Computing Technologies in Agriculture (CCTA)

作者： Lv, Ping Liu, Dong NE Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Heilongjiang Peoples R China

ISBN: (纸本)9783642272806

According to the imbalance development and utilization of water resources, water shortages and other issues in Sanjiang Plain, taking Jiansanjiang branch bureau as an example, the multi-objective optimal allocation model of water resources is established with goal of maximum economic and social benefits. Only surface water, groundwater and transit water are considered overall and different water demands in industry, life and agriculture are satisfied can we realize the rational allocation of regional water resources. The large system decomposition-coordination theory and multi-objective genetic algorithm are applied to solve the model. The optimization results showed that, the water shortage situation in Jiansanjiang branch bureau is improved in planning years and surface water supply capacity can be increased gradually and groundwater resources can be effectively protected. The optimal allocation model and solution method are effective and feasible, and the optimal allocation results are reasonable. The research can provide scientific basis for rational development and utilization of water resources in Jiansanjiang branch bureau and Sanjiang Plain.

关键词： Jiansanjiang branch bureau optimal allocation of water resources multi-objective genetic algorithm large system decomposition-coordination

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Entropy based solid transportation problems with discounted unit costs under fuzzy random environment

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OPSEARCH 2014年第4期51卷 479-532页

作者： Giri, Pravash Kumar Maiti, Manas Kumar Maiti, Manoranjan Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Medinipur W Bengal India Mahishadal Raj Coll Dept Math Medinipur 721628 W Bengal India

This paper investigates some multi-objective fixed charge solid transportation problems (FCSTPs) under fuzzy-random environment with fuzzy-random fixed charges and transportation times. Here objectives are total transportation cost and time which are minimized. Unit transportation costs are crisp and three types of discount i.e. All Unit Discount (AUD), Incremental Quantity Discount (IQD), IQD within AUD are applied upon these costs. In the first model (Model-I) supplies, demands and capacities of conveyances are assumed to be fuzzy-random in nature. These quantities are fuzzy in the second model (ModelII). Imprecise objectives of the models are transformed into equivalent crisp ones using random expectation and fuzzy possibility/necessity measures on fuzzy event. Imprecise constraints are reduced to equivalent crisp constraints using two different fuzzy-random chance constraint methods. Similarly constraints of Model-II are reduced to equivalent crisp constraints with the use of possibility and necessity measures on fuzzy events. Models are also formulated with and without entropy function. Finally transformed constrained multi-objective deterministic problems are solved using a multi-objective genetic algorithm (MOGA) based on arithmetic crossover and boundary mutation. Moreover, Model-IA1 is converted to a single objective non-linear programming (SONLP) problem following fuzzy non-linear programming (FNLP) technique and the reduced ModelIA1S is solved using generalized reduced gradient (GRG) method (using LINGO software). Numerical examples are used for illustration and comparison of the models.

关键词： Solid transportation problem Fuzzy random variable All unit discount Incremental quantity discount Entropy Fuzzy programming technique multi-objective genetic algorithm

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A Study on TODO Task Management at Home

A Study on TODO Task Management at Home

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International Conference of Soft Computing and Pattern Recognition

作者： Ajikata, Sayaka Kobayashi, Ichiro Ochanomizu Univ Grad Sch Humanities & Sci Kobayashi Lab Adv SciBunkyo Ku 2-1-1 Ohtsuka Tokyo 1128610 Japan Ochanomizu Univ Grad Sch Humanities & Sci Adv Sci Bunkyo Ku Tokyo 1128610 Japan

ISBN: (纸本)9781424453306

In the small community like a family, there exist several TODO tasks to be performed cooperatively by the members for making the community life easier. The TODO tasks have to be performed by someone in the community, therefore, it is preferable that the tasks should be done by the members without a big burden. In this context, we focus on TODO task management in a family, and propose a method to make a schedule in which the family members cooperate each other to achieve TODO tasks by taking account of multiple constraints, e.g., members' expert ability, schedules, etc. To make such a schedule, we use multi-objective genetic algorithm.

关键词： multi-objective genetic algorithm TODO task assignment family

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Stochastic multi-objective production-distribution network design using simulation-based optimization

Stochastic multi-objective production-distribution network d...

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12th IFAC/IFIP/IFORS/IEEE Symposium on Information Control Problems in Manufacturing (INCOM 2006)

作者： Ding, Hongwei Benyoucef, Lyes Xie, Xiaolan ISGMP COSTEAM Project INRIA Metz France IBM China Res Lab Beijing Peoples R China Ctr Hlth Sci & Engn St Etienne France

This paper addresses the design of production-distribution networks including both supply chain configuration and related operational decisions such as order splitting, transportation allocation and inventory control. The goal is to achieve the best compromise between cost and customer service level. An optimization methodology that combines a multi-objective genetic algorithm (MOGA) and simulation is proposed to optimize not only the structure of the production-distribution network but also its operation strategies and related control parameters. A flexible simulation framework is developed to enable the automatic simulation of the production-distribution network with all possible configurations and all possible control strategies. To illustrate its effectiveness, the proposed method is applied to a real life case study from automotive industry.

关键词： Production-distribution network design Supply chains multi-objective genetic algorithm Simulation-based optimization

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