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Hedging foreign exchange rate risk: Multi-currency diversification

EUROPEAN JOURNAL OF MANAGEMENT AND BUSINESS ECONOMICS 2016年第1期25卷 2-7页

作者： Alvarez-Diez, Susana Alfaro-Cid, Eva Fernandez-Blanco, Matilde O. Univ Murcia Dept Quantitat Methods Econ Campus Espinardo E-30100 Murcia Spain Inst Technol Informat Camino Vera S-N Valencia 46022 Spain Univ Valencia Dept Corp Finance Avda Tarongers S-N Valencia 46022 Spain

This article proposes a multi-currency cross-hedging strategy that minimizes the exchange risk. The use of derivatives in small and medium-sized enterprises (SMEs) is not common but, despite its complexity, can be interesting for those with international activities. In particular, the reduction in the exchange risk borne through the use of natural multi-currency cross-hedging is measured, considering Conditional Value-at-Risk (CVaR) and Value-at-Risk (VaR) for measuring market risk instead of the variance. CVaR is minimized using linear programmes, while a multiobjective genetic algorithm is designed for minimizing VaR, considering two scenarios for each currency. The results obtained show that the optimal hedge strategy that minimizes VaR is different from the minimum CVaR hedge strategy. A very interesting point is that, just by investing in other currencies, a significant risk reduction in VaR and CVaR can be obtained. (c) 2015 AEDEM. Published by Elsevier Espana, S.L.U.

关键词： Cross-hedging Conditional Value-at-Risk Value-at-Risk Multi-currency diversification multiobjective genetic algorithm

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OPTIMIZATION OF SHORT-HAUL AIRLINE CREW PAIRING PROBLEMS USING A multiobjective genetic algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2010年第9期6卷 3943-3959页

作者： Chen, Chiu-Hung Chou, Ta-Yuan Liu, Tung-Kuan Chou, Jyh-Horng Lee, Chung-Nan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 804 Taiwan Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung 824 Taiwan

Airline crew pairing problems involve optimizing an overall evaluation function containing various conflicting objectives and constraints originating from cost and safety considerations. Classical approaches based on set partitioning or set covering methods separate the solution into two phases, pairing generation and pairing optimization, and evaluate the cost by a weighted-sum of objective values. This paper proposes a new multiobjective evolutionary approach to improve the classical solution flow by integrating the two-phase steps as a single step and reasoning the multiple practical objectives simultaneously. Furthermore, this paper also examines real-life daily pairing problems in a Taiwanese short-haul airline as case studies. Compared to man-made pairing plans, the positive experimental results demonstrate the more appropriate and effective crew pairing plans explored according to practical considerations. These considerations include objectives such as duty connection, transition time, layover, pairing number, aircraft changing limes, flying time, and duty period.

关键词： Crew pairing multiobjective genetic algorithm Short-haul airline

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multiobjective genetic algorithm-Based Fuzzy Clustering of Categorical Attributes

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2009年第5期13卷 991-1005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 India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

Recently, the problem of clustering categorical data, where no natural ordering among the elements of a categorical attribute domain can be found, has been gaining significant attention from researchers. With the growing demand for categorical data clustering, a few clustering algorithms with focus on categorical data have recently been developed. However, most of these methods attempt to optimize a single measure of the clustering goodness. Often, such a single measure may not be appropriate for different kinds of datasets. Thus, consideration of multiple, often conflicting, objectives appears to be natural for this problem. Although we have previously addressed the problem of multiobjective fuzzy clustering for continuous data, these algorithms cannot be applied for categorical data where the cluster means are not defined. Motivated by this, in this paper a multiobjective genetic algorithm-based approach for fuzzy clustering of categorical data is proposed that encodes the cluster modes and simultaneously optimizes fuzzy compactness and fuzzy separation of the clusters. Moreover, a novel method for obtaining the final clustering solution from the set of resultant Pareto-optimal solutions in proposed. This is based on majority voting among Pareto front solutions followed by k-nn classification. The performance of the proposed fuzzy categorical data-clustering techniques has been compared with that of some other widely used algorithms, both quantitatively and qualitatively. For this purpose, various synthetic and real-life categorical datasets have been considered. Also, a statistical significance test has been conducted to establish the significant superiority of the proposed multiobjective approach.

关键词： Categorical attributes fuzzy clustering multiobjective genetic algorithm Pareto optimality

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Implementation and testing of a soft computing based model predictive control on an industrial controller

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JOURNAL OF APPLIED LOGIC 2015年第2期13卷 114-125页

作者： Larrea, M. Larzabal, E. Irigoyen, E. Valera, J. J. Dendaluce, M. Univ Basque Country UPV EHU Intelligent Control Res Grp GICI Leioa Spain

This work presents a real time testing approach of an Intelligent multiobjective Nonlinear-Model Predictive Control Strategy (iMO-NMPC). The goal is the testing and analysis of the feasibility and reliability of some Soft Computing (SC) techniques running on a real time industrial controller. In this predictive control strategy, a multiobjective genetic algorithm is used together with a Recurrent Artificial Neural Network in order to obtain the control action at each sampling time. The entire development process, from the numeric simulation of the control scheme to its implementation and testing on a PC-based industrial controller, is also presented in this paper. The computational time requirements are discussed as well. The obtained results show that the SC techniques can be considered also to tackle highly nonlinear and coupled complex control problems in real time, thus optimising and enhancing the response of the control loop. Therefore this work is a contribution to spread the SC techniques in on-line control applications, where currently they are relegated mainly to be used off-line, as is the case of optimal tuning of control strategies. (C) 2014 Elsevier B.V. All rights reserved.

关键词： multiobjective genetic algorithm Neural network NMPC HiL testing Industrial controller

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High Efficiency Short Backfire Antenna Using Electromagnetically Hard Walls

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2015年 14卷 1614-1617页

作者： Bray, Matthew G. Lier, Erik Lockheed Martin Space Syst Newtown PA 18940 USA

This letter presents a high efficiency short backfire antenna (SBFA) using electromagnetically hard walls. A hard surface composed of low permittivity dielectric liner with longitudinal metal surface strips is affixed to the inside walls of a conventional SBFA's main reflector. The hard-walled SBFA was optimized to operate at L1 and L2 GPS bands with circular polarization using an R2 indicator based evolutionary multiobjective algorithm. A hard-walled SBFA with 2 lambda aperture diameter at L1 achieved 90% and 98% aperture efficiency at L1 and L2, respectively. Also the antenna exhibited more than 90% aperture efficiency over 16% bandwidth around L1. Compared to a conventional SBFA with the same aperture diameter the new antenna achieves 1.25 dB higher directivity at L1 and 0.76 dB at L2, as well as improved cross-polarization due to a more uniform aperture distribution.

关键词： Hard electromagnetic surface multiobjective genetic algorithm short backfire antenna strip-loaded wall

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Improving distance based image retrieval using non-dominated sorting genetic algorithm

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PATTERN RECOGNITION LETTERS 2015年第Feb.1期53卷 109-117页

作者： Arevalillo-Herraez, Miguel Ferri, Francesc J. Moreno-Picot, Salvador Univ Valencia Dept Informat E-46100 Burjassot Spain

Relevance feedback has been adopted as a standard in Content Based Image Retrieval (CBIR). One major difficulty that algorithms have to face is to achieve and adequate balance between the exploitation of already known areas of interest and the exploration of the feature space to find other relevant areas. In this paper, we evaluate different ways to combine two existing relevance feedback methods that place unequal emphasis on exploration and exploitation, in the context of distance based methods. The hybrid approach proposed has been evaluated by using three image databases of various sizes that use different descriptors. Results show that the hybrid technique performs better than any of the original methods, highlighting the benefits of combining exploitation and exploration in relevance feedback tasks. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Image retrieval multiobjective genetic algorithm Relevance feedback

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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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Multi-objective optimization of evaporator of organic Rankine cycle (ORC) for low temperature geothermal heat source

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APPLIED THERMAL ENGINEERING 2015年 80卷 1-9页

作者： Imran, Muhammad Usman, Muhammad Park, Byung-Sik Kim, Hyouck-Ju Lee, Dong-Hyun Korea Univ Sci & Technol Energy Syst Engn Taejon 305350 South Korea Korea Inst Energy Res Energy Network Lab Taejon 305343 South Korea

Present study deals with the development of hydraulic and thermal design model of chevron type plate evaporator and optimization of its geometrical parameters for a low temperature geothermal ORC system. The optimization is performed using Non-dominated Sorting genetic algorithm-II (NSGA-II). The primary geometrical parameters of evaporator are selected as decision variables which include length, width and plate spacing. The minimum cost of evaporator and minimum pressure drop are chosen as objective functions under restriction of constant amount of heat transfer. Since the objective functions are conflicting, a single value of decision variables cannot satisfy both objective functions simultaneously. Therefore optimization results are presented in the form of Pareto Front solution which is trade-off between pressure drop and cost of evaporator. The minimum cost evaporator is 1570$ corresponding to pressure drop of 125 kPa while the maximum cost is 6988$ corresponding to a pressure drop of 5.2 kPa. The sensitivity analysis shows that the plate length has promising effect on pressure drop and cost of evaporator. Furthermore, the effect of pressure drop on cost of evaporator and net power of ORC system is also investigated. The optimum value of allowable pressure drop is 30-40 kPa, corresponding to net power of 73-74 kW of ORC system and cost of evaporator 3000-3500$. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Organic Rankine cycle Evaporator Optimization Geothermal energy multiobjective genetic algorithm

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A multiobjective optimization approach for location-allocation of clinics

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2015年第2期22卷 313-328页

作者： Beheshtifar, Sara Alimoahmmadi, Abbas KN Toosi Univ Technol Fac Geodesy & Geomat Tehran Iran

In order to establish new healthcare facilities, their optimal number and locations should be determined. Unsuitable locations for these facilities may result in substandard customer services and increased expenses. To solve this location-allocation problem, this study applied a multiobjective model that combined geographical information system (GIS) analysis with amultiobjective genetic algorithm. Optimum sites for new clinics were determined by considering four objectives: minimizing total travel cost, minimizing inequity in access to clinics, minimizing the land-use incompatibility in the study area, and minimizing the costs of land acquisition and facility establishment. Chromosomes of varying lengths were used in the multiobjective optimization process. An important advantage of this is that multiple optimal solutions with different numbers of healthcare facilities can be compared directly. An a posteriori preference method was used in this study. TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) was applied to assess and compare the Pareto-optimal solutions and to select the best solution according to different weight vectors. Visualization of the best solution according to each weight vector and compromising among different objectives provided valuable possibilities for selection of the best alternative to decision makers.

关键词： multiobjective genetic algorithm location modeling GIS TOPSIS

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Application of Evolutionary algorithm to multiobjective Optimization of Hydraulic Actuation System

Application of Evolutionary Algorithm to Multiobjective Opti...

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International Conference on Mechanical Engineering, Automation and Control Systems (MEACS)

作者： Averchenkov, V. Kazakov, P. Kazakov, V. Reutov, A. Lozbinev, F. Bryansk State Tech Univ Dept Informat Technol Bryansk Russia Bryansk State Tech Univ Power Engn & Elect Dept Bryansk Russia

ISBN: (纸本)9781467381147

An approach to improve the quality of the design hydraulic actuator by means of solving this problem as a multiobjective is suggested. A mathematical model of multiobjective optimization design and operating parameters of the hydraulic actuator is considered. All the set of Pareto-optimal alternatives is obtained for decision making in selection of the best variant of hydraulic actuator. The special genetic algorithm for Pareto-set obtaining was developed.

关键词： hydraulic actuator stability and power intensity of a hydraulic actuator multiobjective optimization Pareto set multiobjective genetic algorithm

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