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21st IEEE Symposium on Communications and Vehicular Technology in the BeNeLux (IEEE SCVT)

作者： Chiumento, Alessandro Blanch, Carolina Desset, Claude Pollin, Sofie Van der Perre, Liesbet Lauwereins, Rudy Interuniv Microelect Ctr IMEC Vzw Kapeldreef 75 B-3001 Leuven Belgium Katholieke Univ Leuven Dept Elect Engn ESAT Leuven Belgium

ISBN: (纸本)9781479980307

Resource allocation in LTE networks is a challenging problem as multiple users, under a variety of channel conditions, compete for scarce network resources. Moreover, targeting the optimization of the network capacity while still guaranteeing a minimum quality of service complicates the problem considerably. Traditional schedulers can achieve a single trade-off between the conflicting capacity optimization and fairness objectives. This paper presents a novel allocation strategy based on genetic algorithms in which both objectives are simultaneously optimized. This is done by maximizing both the total cell rate and cell-edge rate. The results show that the proposed algorithm offers a range of Pareto optimal solutions that outperform all other reference strategies.

关键词： Long Term Evolution Pareto optimisation genetic algorithms quality of service resource allocation telecommunication scheduling LTE networks Pareto optimal solutions cell rate cell-center trade-off cell-edge multiobjective genetic algorithm network capacity optimization Europe Interference OFDM Phase shift keying Signal to noise ratio Resource utilization Quality of service genetic algorithm (GA) Long Term Evolution Pareto optimisation management of resources signal to noise ratios Phase shift keying Local thermodynamic equilibrium Orthogonal frequency division multiplexing optimal solution Cell rates network capacity

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Multi-Objective Optimal Powertrain Design of Parallel Hybrid Vehicles with Respect to Fuel Consumption and Driving Performance 13

Multi-Objective Optimal Powertrain Design of Parallel Hybrid...

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13th European Control Conference (ECC)

作者： Boehme, Thomas J. Frank, Benjamin Schori, Markus Jeinsch, Torsten IAV Automot Engn Dept Gasoline Engine Syst D-38518 Gifhorn Germany Univ Rostock Inst Automatisierungstech D-18119 Rostock Germany

ISBN: (纸本)9783952426913

In the past decade, Hybrid Electric Vehicles have been demonstrated to significantly reduce the fuel consumption and emissions. However, this capability strongly depends on the sizing of the components and on the quality of the energy management. These challenges require new optimization procedures for a systematical exploration of the design space to find the optimal component sizings and control trajectories. A novel two-layer optimization strategy based on a multi-objective problem formulation is proposed. The first layer consists of a multi-objective genetic algorithm for determining the best system design parameters with respect to fuel consumption and driving performance. The second layer solves a deterministic hybrid optimal control problem (HOCP) to find for each individual of the population pool the optimal continuous and discrete control trajectories for the energy management. The proposed optimization strategy is benchmarked to a one-layer genetic algorithm approach on a parallel hybrid design study.

关键词： genetic algorithms hybrid electric vehicles optimal control power transmission (mechanical) design space deterministic hybrid optimal control problem discrete control trajectories driving performance energy management fuel consumption multiobjective genetic algorithm multiobjective optimal powertrain design multiobjective problem formulation one-layer genetic algorithm approach optimal component sizings parallel hybrid vehicles system design parameters two-layer optimization strategy Energy management Fuels Gears Ice Mechanical power transmission Optimization Torque Hybrid electric vehicles Energy management Fuel consumption genetic algorithm (GA) Powertrain Gears Optimal control Fuels Torque Ice design parameter

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AN APPLICATION OF genetic algorithm TOWARD SOLVING THE RELIABILITY PROBLEM OF multiobjective SERIES-PARALLEL SYSTEMS

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EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 2012年第3期14卷 243-248页

作者： Zarinchang, Amir Faghih, Nezameddin Zarinchang, Jafar Islamic Azad Univ Shiraz Branch Dept Ind Engn Shiraz Iran Shiraz Univ Dept Management Shiraz Iran Shiraz Univ Dept Mech Engn Shiraz Iran

Since developing an appropriate solution for reliability optimization problem with mathematical programming methods has been considered as difficult techniques, the heuristic approaches increasingly has been applied. Multiobjectve genetic algorithm (MGA) has been among heuristic methods that was developed to find solutions for series-parallel systems to obtain maximum reliability, and minimum cost and weight at the system level. These are very common problems in engineering design such as mechanical and electrical systems. It has been shown that the Multiobjectve genetic algorithm offers proper results to these problems while it respects to the several objective functions such as reliability, cost and weight. This paper presents the combination of probabilistic search, and one of the decision making methods called Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The Multiobjectve genetic algorithm, allows us to achieve a proper design solution while it saves a considerable time compared with some other approaches. At the same time as the reliability, cost and weight were chosen as objective functions, the results obtained by this method showed an overall improvement in comparison to the existing GA method considering cost and weight as constraints.

关键词： multiobjective genetic algorithm Reliability Optimization Redundancy Apportionment Series-Parallel Systems TOPSIS Method

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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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An Interactive Approach to multiobjective Clustering of Gene Expression Patterns

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2013年第1期60卷 35-41页

作者： Mukhopadhyay, Anirban Maulik, Ujjwal Bandyopadhyay, Sanghamitra Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 W Bengal India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India

Some recent studies have posed the problem of data clustering as a multiobjective optimization problem, where several cluster validity indices are simultaneously optimized to obtain tradeoff clustering solutions. A number of cluster validity index measures are available in the literature. However, none of the measures can perform equally well in all kinds of datasets. Depending on the dataset properties and its inherent clustering structure, different cluster validity measures perform differently. Therefore, it is important to find the best set of validity indices that should be optimized simultaneously to obtain good clustering results. In this paper, a novel interactive genetic algorithm-based multiobjective approach is proposed that simultaneously finds the clustering solution as well as evolves the set of validity measures that are to be optimized simultaneously. The proposed method interactively takes the input from the human decision maker (DM) during execution and adaptively learns from that input to obtain the final set of validity measures along with the final clustering result. The algorithm is applied for clustering real-life benchmark gene expression datasets and its performance is compared with that of several other existing clustering algorithms to demonstrate its effectiveness. The results indicate that the proposed method outperforms the other existing algorithms for all the datasets considered here.

关键词： Clustering interactive algorithm microarray gene expression multiobjective genetic algorithm Pareto optimality

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Multi objective outbound logistics network design for a manufacturing supply chain

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JOURNAL OF INTELLIGENT MANUFACTURING 2013年第6期24卷 1071-1084页

作者： Hiremath, N. C. Sahu, Sadananda Tiwari, Manoj Kumar Indian Inst Technol Kharagpur Dept Ind Engn & Management Kharagpur 721302 W Bengal India

Outbound logistics network (OLN) in the downstream supply chain of a firm plays a dominant role in the success or failure of that firm. This paper proposes the design of a hybrid and flexible OLN in multi objective context. The proposed distribution network for a manufacturing supply chain consists of a set of customer zones (CZs) at known locations with known demands being served by a set of potential manufacturing plants, a set of potential central distribution centers (CDCs), and a set of potential regional distribution centers (RDCs). Three variants of a single product classified based on nature of demand are supplied to CZs through three different distribution channels. The decision variables include number of plants, CDCs, RDCs, and quantities of each variant of product delivered to CZs through a designated distribution channel. The goal is to design the network with multiple objectives so as to minimize the total cost, maximize the unit fill rates, and maximize the resource utilization of the facilities in the network. The problem is formulated as a mixed integer linear programming problem and a multiobjective genetic algorithm (MOGA) called non-dominated sorting genetic algorithm-II (NSGA-II) is employed to solve the resulting NP-hard combinatorial optimization problem. Computational experiments conducted on randomly generated data sets are presented and analyzed showing the effectiveness of the solution algorithm for the proposed network.

关键词： Outbound logistics network (OLN) Outbound logistics network design (OLND) Manufacturing supply chain Mixed integer linear programming multiobjective genetic algorithm NSGA-II

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multiobjective Short-Term Planning of Electric Power Distribution Systems Using NSGA-II

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2013年第3期24卷 286-299页

作者： Pereira Junior, Benvindo R. Cossi, Antonio M. Mantovani, Jose R. S. Sao Paulo State Univ UNESP Elect Engn Dept POB 31 BR-15385000 Ilha Solteira SP Brazil Sao Paulo State Univ UNESP Math Dept BR-15385000 Ilha Solteira SP Brazil

This paper presents a mixed integer nonlinear programming multiobjective model for short-term planning of distribution networks that considers in an integrated manner the following planning activities: allocation of capacitor banks;voltage regulators;the cable replacement of branches and feeders. The objective functions considered in the proposed model are: to minimize operational and investment costs and minimize the voltage deviations in the the network buses, subject to a set of technical and operational constraints. A multiobjective genetic algorithm based on a Non-Dominated Sorting genetic algorithm (NSGA-II) is proposed to solve this model. The proposed mathematical model and solution methodology is validated testing a medium voltage distribution system with 135 buses.

关键词： Capacitor banks Voltage regulators multiobjective genetic algorithm Power distribution planning

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Strategies for multiobjective genetic algorithm development: Application to optimal batch plant design in process systems engineering

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COMPUTERS & INDUSTRIAL ENGINEERING 2008年第3期54卷 539-569页

作者： Dietz, A. Azzaro-Pantel, C. Pibouleau, L. Domenech, S. CNRS INPT UPS UMR 5503 Lab Genie Chim F-31106 Toulouse 1 France

This work deals with multiobjective optimization problems using genetic algorithms (GA). A multiobjective GA (MOGA) is proposed to solve multiobjective problems combining both continuous and discrete variables. This kind of problem is commonly found in chemical engineering since process design and operability involve structural and decisional choices as well as the determination of operating conditions. In this paper, a design of a basic MOGA which copes successfully with a range of typical chemical engineering optimization problems is considered and the key points of its architecture described in detail. Several performance tests are presented, based on the influence of bit ranging encoding in a chromosome. Four mathematical functions were used as a test bench. The MOGA was able to find the optimal solution for each objective function, as well as an important number of Pareto, optimal solutions. Then, the results of two multiobjective case studies in batch plant design and retrofit were presented, showing the flexibility and adaptability of the MOGA to deal with various engineering problems. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： multiobjective genetic algorithm multiobjective optimization batch plant design Pareto sort procedure

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A Hybrid multiobjective genetic algorithm on Optimizing Aircraft Schedule Recovery Problems under Short-Time Response

A Hybrid Multiobjective Genetic Algorithm on Optimizing Airc...

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IEEE International Conference on System, Man, and Cybernetic

作者： Chen, Chiu-Hung Liu, Tung-Kuan Chou, Jyh-Horng Tsai, Jinn-Tsong Ho, Wen-Hsien Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung Taiwan Natl Pingtung Univ Educ Dept Comp Sci Pingtung Taiwan Kaohsiung Med Univ Dept Med Informat Management Kaohsiung Taiwan

ISBN: (纸本)9781424423835

This article presents a hybrid multiobjectivc genetic algorithm to aid the tracking of the daily aircraft schedule recovery problem under disturbance events such as severe weather and mechanical problems. The proposed algorithm extends from the original method of inequality-based multiobjective genetic algorithm (MMGA) and utilizes an adaptive evaluated vector (AEV) to co-work with MMGA efficiently when maintaining the Pareto set of recovered schedules in the evolutionary population. Two main goals would be presented: One is to provide a multi-objective solution to the recovery problem and the other is to address the performance requirement on the recovery approach. A simulated disturbance experiment on the practical aircraft schedule is made to validate the recovery results under the expected short-time period.

关键词： multiobjective genetic algorithm Aircraft Schedule Recovery Pareto Set

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AN APPROACH TO COMPUTER-AIDED PROCESS PLANNING IN DISTRIBUTED MANUFACTURING USING multiobjective genetic algorithm

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38th International Conference on Computers and Industrial Engineering

作者： Zhang, Wenqiang Gen, Mitsuo Lin, Lin Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9787121074370

In a distributed manufacturing environment, factories possessing various machines and tools at different geographical locations are often combined to achieve the highest production efficiency. When jobs requiring several operations are received, feasible process plans are produced by those available factories and different resource constraints. Therefore, obtaining an optimal or near-optimal process plan becomes important. This paper presents a multiobjective genetic algorithm (moGA) using a random key-based representation method, which, according to prescribed criteria such as minimizing processing time, minimizing production cost, minimizing transportation time and cost between factories, could swiftly search for the optimal process plan in distributed manufacturing systems. The process plan is decided by operations sequence, factory selection, machine selection, tool selection and tool access direction selection. By applying the moGA, the computer-aided process planning (CAPP) system can generate optimal or near-optimal process plans.

关键词： Computer-Aided Process Planning Distributed Manufacturing multiobjective genetic algorithm Operation Sequence

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