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MULTI-OBJECTIVE RECONFIGURABLE CONTROLLER DESIGN FOR THREE-TANK SYSTEM USING PARAMETRIC EIGENSTRUCTURE ASSIGNMENT

CONTROL AND INTELLIGENT SYSTEMS 2011年第1期39卷 12-22页

作者： Sutha, Subbian Thyagarajan, Thangavelu Department of Instrumentation Engineering Madras Institute of Technology Chennai 600044 Tamil Nadu India

The reconfigurable controller (RC) design of strongly interacting multi-input multi-output system is a challenging problem for the designer. In this paper, a parametric right eigenstructure assignment based multiobjective dynamic reconfigurable controller through state feedback is proposed against partial actuator failure for a benchmark three-tank system. The practical aspects of controller design require the conflicting objectives of robust stability to parameter perturbation and smaller controller gain to improve the transient response of the system. These design objectives rise to the minimum condition number of closed-loop eigenvector and minimum norm of feedback controller gain of closed-loop system respectively. Fast and elitist multiobjective genetic algorithm known as nondominated sorting genetic algorithm-II is successfully applied to select the pareto-optimal eigenstructure of closed-loop system. Further, RC has been designed to accommodate partial actuator failure which is considered as loss of control effectiveness in actuators. Added value of this work, the controller uses complex-valued chromosomes. The effectiveness of the proposed controller is illustrated for different actuator fault scenarios through simulation and its performance is analysed.

关键词： Parametric eigenstructure assignment reconfigurable controller multiobjective genetic algorithm nondominated sorting genetic algorithm multiobjective dynamic reconfigurable controller

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The green and economical supply of coated board with intermodal distribution: an implementation of novel evolutionary algorithm

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OPERATIONAL RESEARCH 2024年第2期24卷 19-19页

作者： Shoukat, Rizwan Zhang, Xiaoqiang Southwest Jiaotong Univ Sch Transportat & Logist Chengdu Peoples R China Natl Engn Lab Integrated Transportat Big Data Appl Chengdu Peoples R China Lab Natl United Engn Lab Integrated & Intelligent 111 North Second Ring Rd Chengdu Peoples R China

In practice, factories emit significant amounts of GHGs, which have been linked to health and environmental issues. These gases are produced during industrial processes and, in many cases, during transportation. Second, historically, generic costs such as production fixed and variable costs, transportation costs, and inventory holding costs have dominated the overall cost of the supply chain (SC). The objective of this research is to model a SC problem to minimize costs while also accounting for GHG emissions from facility to facility (Shanghai, China to Lahore, Pakistan). Initially, the total expenses and GHG emissions of the SC are modeled as a bi-objective mixed-integer linear programme (BOMILP). In comparison to other optimization models, the developed BOMILP model simultaneously optimizes transportation costs and GHG emissions while taking weight and distance constraints into account in the intermodal network. The real-world data comes from one of Asia's largest paper and board industries. To solve the BOMILP problem, a metaheuristic technique such as a multiobjective genetic algorithm (MOGA) and multiobjective bat algorithm (MOBA) is used. To improve the performance of the MOGA, we used multiple crossover operators. Random crossover operator selection and collision crossover are two strategies that have been implemented. The collision crossover is based on elastic collision, and the random crossover operator-selection strategy randomly selects the best operator. The Pareto optimum solution of the MOGA assists decision-makers in making the best trade-off between cost and emissions. Our findings show that the cost of producing (reeling, coating, rewinding) and storing white bleach board (WBB) accounts for 72% of the total cost of the SC, while GHG emissions are 83,484 kg CO2e in the delivery of 1 tonne of material from Shanghai, China to Lahore, Pakistan via intermodal transportation.

关键词： Transportation costs GHG emissions Metaheuristic technique multiobjective genetic algorithm Pareto optimum solution

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Hybrid Flow Shop with Unrelated Machines, Setup Time, and Work in Progress Buffers for Bi-Objective Optimization of Tortilla Manufacturing

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algorithmS 2018年第5期11卷 68-68页

作者： Yaurima-Basaldua, Victor Hugo Tchernykh, Andrei Villalobos-Rodriguez, Francisco Salomon-Torres, Ricardo Sonora State Univ Software Engn San Luis Rio Colorado 83455 Sonora Mexico CICESE Res Ctr Comp Sci Dept Ensenada 22860 Baja California Mexico South Ural State Univ Sch Elect Engn & Comp Sci Chelyabinsk 454080 Russia

We address a scheduling problem in an actual environment of the tortilla industry. Since the problem is NP hard, we focus on suboptimal scheduling solutions. We concentrate on a complex multistage, multiproduct, multimachine, and batch production environment considering completion time and energy consumption optimization criteria. The production of wheat-based and corn-based tortillas of different styles is considered. The proposed bi-objective algorithm is based on the known Nondominated Sorting genetic algorithm II (NSGA-II). To tune it up, we apply statistical analysis of multifactorial variance. A branch and bound algorithm is used to assert obtained performance. We show that the proposed algorithms can be efficiently used in a real production environment. The mono-objective and bi-objective analyses provide a good compromise between saving energy and efficiency. To demonstrate the practical relevance of the results, we examine our solution on real data. We find that it can save 48% of production time and 47% of electricity consumption over the actual production.

关键词： multiobjective genetic algorithm hybrid flow shop setup time energy optimization production environment

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Design Efficient Technologies for Context Image Analysis in Dialog HCI Using Self-Configuring Novelty Search genetic algorithm

Design Efficient Technologies for Context Image Analysis in ...

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International Conference on Informatics in Control, Automation and Robotics

作者： Evgeny Sopov Ilia Ivanov Institute of Computer Science and Telecommunications Siberian State Aerospace University

ISBN: (纸本)9781479979202

The efficiency of HCI systems can be sufficiently improved by the analysis of additional contextual information about the human user and interaction process. The processing of visual context provides HCI with such information as user identification, age, gender, emotion recognition and others. In this work, an approach to adaptive model building for image classification is presented. The novelty search based upon the multi-objective genetic algorithm is used to stochastically design a variety of independent technologies, which provide different image analysis strategies. Finally, the ensemble based decision is built adaptively for the given image analysis problem.

关键词： Image Analysis Classification multiobjective genetic algorithm Novelty Search

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Designing Combinatorial Libraries Optimized on Multiple Objectives

Designing Combinatorial Libraries Optimized on Multiple Obje...

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作者： Valerie J. Gillet

The recent emphasis in combinatorial library design has shifted from the design of very large diverse libraries to the design of smaller more focused libraries. Typically the aim is to incorporate as much knowledge into the design as possible. This knowledge may relate to the target protein itself or may be derived from known active and inactive compounds. Other factors should also be taken into account, such as the cost of the library and the physicochemical properties of the compounds that are contained within the library. Thus, library design is a multiobjective optimization problem. Most approaches to optimizing multiple objectives are based on aggregation methods whereby the objectives are assigned relative weights and are combined into a single fitness function. A more recent approach involves the use of a multiobjective genetic algorithm in which the individual objectives are handled independently without the need to assign weights. The result is a family of solutions each of which represents a different compromise in the objectives. Thus, the library designer is able to make an informed choice on an appropriate compromise solution. less

关键词： Simulated Annealing Monte Carlo Simulations Combinatorial libraries combinatorial synthesis genetic algorithms molecular descriptors computational filtering druglike library enumeration MOGA multiobjective genetic algorithm multiobjective optimization

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Construction of Variable-Length Error-Correcting Codes using MOGA

Construction of Variable-Length Error-Correcting Codes using...

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Mediterranean Electrotechnical Conference

作者： Paula Vella Victor Buttigieg Dept. of Comm. & Comp. Eng. University of Malta Msida MSD 2080 Malta

ISBN: (纸本)9781509000593

Variable-length codes are classically used for source coding to compress data. However, Variable-Length Error-Correcting (VLEC) codes can also be used for channel coding where they are able to overcome the effects of channel impairments by detecting and correcting bit errors. Multi-objective optimization is an area of multiple criteria decision making, where an optimization algorithm attempts to solve a problem with more than one objective. In this paper, VLEC codes are constructed by taking into consideration their two main properties: Average Codeword Length and Free Distance, associated respectively with their source and channel coding aspects. These two properties are taken as objectives in a Multi-objective global optimization genetic algorithm. It is shown that better codes can be constructed using this algorithm.

关键词： variable-length error-correcting codes multiobjective genetic algorithm code construction

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Survivable and Impairment-Aware Virtual Topologies for Reconfigurable Optical Networks: a Cognitive Approach

Survivable and Impairment-Aware Virtual Topologies for Recon...

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International Congress on Ultra Modern Telecommunications and Control Systems

作者： N. Fernandez R. J. Duran I. de Miguel N. Merayo J. C. Aguado P. Fernandez T. Jiménez I. Rodriguez D. Sanchez R. M. Lorenzo E. J. Abril M. Angelou I. Tomkos University of Valladolid ETSI de Telecomunicación Paseo de Belen 15 47011 Valladolid Spain Athens Information Technology Center 19. 5 km Markopoulou Ave. 19002 Peania Athens Greece

ISBN: (纸本)9781467320160

In this paper, we present a new version of a multiobjective genetic algorithm to design virtual topologies with the aim of reducing both the energy consumption and the network congestion. Moreover, we also propose an improved version of that algorithm, by including two cognitive techniques. Unlike the original version, the new proposals design survivable virtual topologies, as they provide shared path protection thus offering fault-tolerance capability with efficient resource utilization. Moreover, the proposed methods also take into account physical impairments in order to ensure that the survivable virtual topologies fulfill Quality of Transmission requirements in any scenario without failures or with a single cable failure. Finally, a simulation study demonstrates the advantages of the use of cognition.

关键词： Cognition failure protection multiobjective genetic algorithm power consumption QoT requirements virtual topologies

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From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation

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GOVERNMENT INFORMATION QUARTERLY 2022年第2期39卷 101644-101644页

作者： Valle-Cruz, David Fernandez-Cortez, Vanessa Ramon Gil-Garcia, J. Univ Autonoma Estado Mexico Unidad Acad Profes Tianguistenco San Pedro Tlaltizapan Santiago Tiangu Mexico Univ Autonoma Estado Mexico Fac Contaduria & Adm Cerro De Coatepec S-NCiudad Univ Toluca De Lerdo Mexico SUNY Albany Ctr Technol Govt Albany NY 12222 USA SUNY Albany Rockefeller Coll Publ Affairs & Policy Albany NY 12222 USA Univ Americas Puebla Cholula Mexico

Artificial intelligence has become an important tool for governments around the world. However, it is not clear to what extent artificial intelligence can improve decision-making, and some policy domains have not been the focus of most recent studies, including the public budget process. More specifically, budget allocation is one of the areas in which AI may have greatest potential. Therefore, this study attempts to contribute to this gap in our existing knowledge by answering the following research question: To what extent can artificial intelligence techniques help distribute public spending to increase GDP, decrease inflation and reduce the Gini index? In order to respond to this question, this article proposes an algorithmic approach on how budget inputs (specific expenditures) are processed to generate certain outputs (economic, political, and social outcomes). The authors use the multilayer perceptron and a multiobjective genetic algorithm to analyze World Bank Open Data from 1960 to 2019, including 217 countries. The advantages of implementing this type of decision support system in public expenditures allocation arise from the ability to process large amounts of data and to find patterns that are not easy to detect, which include multiple non-linear relationships. Some technical aspects of the expenditure allocation process could be improved with the help of these kinds of techniques. In addition, the results of the AIbased approach are consistent with the findings of the scientific literature on public budgets, using traditional statistical techniques.

关键词： Artificial intelligence Public budgeting E-budgeting Smart budgeting Multilayer perceptron multiobjective genetic algorithm Smartness Budget intelligent machine

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Optimization of the Health Examination Scheduling Problems Using Multi-objective genetic algorithms

Optimization of the Health Examination Scheduling Problems U...

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SICE Annual Conference

作者： Tung-Kuan Liu Ching-Ming Tu Che-Lun Chang Fu-An Chen Chiu-Hung Chen Institute of Engineering Science and Technology Natl. Kaohsiung First Univ. Department of Mechanical and Automation Engineering Natl. Kaohsiung First Univ.

ISBN: (纸本)9784907764388

Traditionally, the health examination scheduling (HES) is manually made by nurses or administrative assistants according to the practical examination situations reported from the medical personnel and the on-site examinees. However, the scheduling results of the man-made plan are usually rough, and hence it is hard to a complete schedule containing the detailed examination time, items and orders. The poor quality of the examination schedule often results in both the waste of medical resources and the bad service. Therefore, the purpose of this paper is to propose an intelligent and automatic approach to solve the HES problem. Our approach adopts a multiobjective genetic algorithm (MOGA) to simultaneously consider various HES management issues and provides a better scheduling plan for the HES problem.

关键词： Health examination scheduling multiobjective genetic algorithm

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Multicriteria Fuzzy Clustering for Brain Image Segmentation

Multicriteria Fuzzy Clustering for Brain Image Segmentation

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International Conference on Modeling, Simulation and Applied Optimization

作者： Olfa Limam Fouad Ben Abdelaziz LARODEC Laboratory Institut Supérieur de Gestion University of Tunis Bardo Tunisia Rouen Business School Rouen France

ISBN: (纸本)9781467358125

One of the most challenging task in image analysis is to identify correctly tissues where boundaries are generally not clear. Fuzzy clustering is supposed to be the most appropriate to model this situation in applications such as tissue classification, tumor detection. While, image segmentation using fuzzy clustering technique classifies correctly pixels of an image with a great extent of accuracy [1], recent works have shown that fuzzy clustering techniques considers a single objective may not provide a good result since no single validity measure works well on different kinds of data sets. Moreover, a wrong choice of a validity measure leads to poor results [2]. In this paper, we introduce a multiobjective fuzzy clustering approach producing a set of Pareto solutions among which the best solution, based on I-index validation measure, is chosen to be the final clustering solution. First, a spatial information is considered to deal more effectively with the noise and intensity inhomogeneities introduced in imaging process. Second, we propose to use a variable string length encoding technique to automatically identify the number of clusters, given that it does not require a prior knowledge about number of clusters present in a data set. Therefore, an initializing method based on a center approximation approach is proposed to accelerate the clustering process and make results more robust. Applied to normal and multiple sclerosis lesion magnetic resonance image brain images, our method shows better performance than competing algorithms.

关键词： Fuzzy c-means clustering multiobjective genetic algorithm MRI brain image

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