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multi-objective optimization for a liner shipping service from different perspectives

Transportation Research Procedia 2017年 25卷 251-260页

作者： Dong-Ping Song Dong Li Paul Drake School of Management University of Liverpool Chatham Street Liverpool L69 7ZH UK

This paper considers the optimization problem for the ship deployment, the planned sailing speed, and the service scheduling in a liner shipping route with both sea and port uncertainties taking multiple objectives from different perspectives. The main purposes are three-folds: first, defining key performance indicators (KPI) in liner shipping service design problem from different stakeholders’ perspectives; second, investigating the relationships between the identified KPIs and their impact on the optimal solutions; third, evaluating the impact of different speed strategies on the KPIs and the optimal solutions.

关键词： Liner shipping service reliability uncertainty multi-objective genetic algorithms

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genetic Fuzzy Modelling of Li-Ion Batteries Through a Combination of Theta-DEA and Knowledge-Based Preference Ordering 17th

Genetic Fuzzy Modelling of Li-Ion Batteries Through a Combin...

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17th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA)

作者： Echevarria, Yuviny Sanchez, Luciano Blanco, Cecilio Univ Oviedo Dept Comp Sci Gijon Spain Univ Oviedo Dept Elect Engn Gijon Spain Univ Cienfuegos Dept Informat Cienfuegos Cuba

ISBN: (纸本)9783319446363;9783319446356

Learning semi-physical fuzzy models of rechargeable Li-Ion batteries from data involves solving a complex multicriteria optimization task where the accuracies of the approximations of the different observable variables are balanced. The fitness function of this problem depends on the recursive evaluation of a set of differential equations, where fuzzy rule-based systems are embedded as nonlinear blocks. Evaluating this function is a time consuming process, thus algorithms that efficiently promote diversity and hence demand a low number of evaluations of the fitness function are preferred. In this paper, a comparison is carried out between some recent genetic algorithms, whose performances are assessed in this particular modelling problem. It is concluded that the combination of the recent.-Dominance Evolutionary Algorithm (theta-DEA) with a Knowledge-based precedence operator, that improves the selection, is a sensible choice. Dominance relations between the Pareto fronts are assessed in terms of binary additive epsilon-quality indicators.

关键词： genetic fuzzy systems Li-Ion battery model multi-objective genetic algorithms Preference orderings

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multi-objective optimization of geometrical parameters of corrugated-undulated heat transfer surfaces

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APPLIED ENERGY 2016年第Jul.15期174卷 25-36页

作者： Wang, Limin Deng, Lei Ji, Chenglong Liang, Erkai Wang, Changxia Che, Defu Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China Harbin Elect Corp Harbin Boiler Preheater Co Ltd Harbin 150046 Peoples R China

To achieve a maximum heat transfer capability and a minimum pumping power for corrugated undulated (CU) heat transfer surfaces, a multi-objectives genetic algorithm was used to obtain the optimal values of the pitch and height of undulated plate (U-plate) and the height of the corrugated plate (C-plate) by using the Pareto optimal strategy. For this purpose, computational fluid dynamics (CFD) simulation, support vector machine (SVM) and the fast non-dominated sorting genetic algorithm were combined together and used for the optimization process. Three dimensional numerical simulations were performed to investigate the effect of geometrical parameters on the thermos-hydraulic performance of CU heat transfer surface. The maximum deviation for the Nusselt number and friction factor between the simulation and the published data were 8.81% and 13.1% respectively when the Reynolds number ranged from 1500 to 10,000. The flow and temperature profile in the CU passage were analyzed. Intensive secondary flows occurred in the C-plate channel and the U-plate channel due to the drag effect between the main flows in the two channels. And the effects of Reynolds number and structure parameters were studied. The change of U-plate height rather than that of U-plate pitch would have a dominant effect on the disturbance influence of U-plate. Besides, two SVM models were trained by the CFD results to predict the Nusselt number and friction factor of flow in CU passages with different geometrical and operational parameters. The comparison between the SVM predictions and the CFD results showed that the SVM models could predict the numerical data with a good accuracy. In addition, two evaluation criteria were proposed from perspectives of the manufacturers and the users, respectively. Finally, a set of optimized solutions were obtained. The optimal values of pumping power ratio and heat transfer area ratio between different CU passages and the standard one were in the range of 0.8-3.1 and

关键词： Corrugated-undulated surfaces Heat transfer Computational fluid dynamics Performance evaluation criteria Support vector machine multi-objective genetic algorithms

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Towards ultrasound hyperthermia safe treatments using computational intelligence techniques 11

Towards ultrasound hyperthermia safe treatments using comput...

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IEEE International Symposium on Medical Measurements and Applications (MeMeA)

作者： Ruano, M. G. Ruano, A. E. Univ Algarve Fac Sci & Technol Faro Portugal Univ Coimbra CISUC Coimbra Portugal Univ Lisbon IDMEC Inst Super Tecn Lisbon Portugal

ISBN: (纸本)9781467391726

A key feature for safe application of hyperthermia treatments is the efficient delimitation of the treatment region avoiding collateral damages. The efficacy of treatment depends on an ultrasound power intensity profile to accomplish the temperature clinically required. Many hyperthermia procedures proposed in the literature rely on a-priori knowledge of the physical properties of tissue. The soft computing models presented in this article are only based on measured data, collected from tissue phantoms reflecting the reactions of human tissues to ultrasounds. From homogeneous to heterogeneous tissues, different soft computing techniques were developed accordingly to experimental constraints. The present state of development is nearly approaching the identification of a computational model to be safety applied in in-vivo hyperthermia sessions.

关键词： Ultrasound Tissue Temperature Modelling Hyperthermia Neural Networks multi-objective genetic algorithms

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Reduction of power consumption and carbon footprints by applying multi-objective optimisation via genetic algorithms

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第2期52卷 337-352页

作者： Liu, Cheng-Hsiang Huang, Ding-Hsiang Natl Pingtung Univ Sci & Technol Dept Ind Management Neipu Taiwan

Firms heavily emphasise reducing carbon footprint, an area warranting further improvement. This study examines carbon footprint within the context of production scheduling. Two multi-objective scheduling problems involving economic- and environmental-related criteria are studied: (1) a batch-processing machine scheduling problem to minimise the total weighted tardiness and carbon footprint simultaneously;(2) a triple-criteria scheduling problem involving of a hybrid flow shop consisting of a batch-processing machine followed by two parallel-processing machines, in which the shop attempts to minimise the total weighted tardiness, carbon footprint and peak power. Since the above problems are treated as a true multi-objective optimisation problem, decision-makers should select a solution among the trade-off solutions provided in the Pareto-optimal set. Therefore, the non-dominated sorting-based genetic algorithm II (NSGA-II) is implemented, which identifies the set of approximate efficient schedules to both multi-objective scheduling problems. Moreover, an adaptive multi-objective genetic algorithm (AMGA) is developed to generate the reference Pareto front, which validates the results that are obtained using NSGA-II. Results of this study demonstrate both the effectiveness of AMGA in converging to the true Pareto-optimal set and the efficiency of NSGA-II.

关键词： scheduling carbon footprint total weighted tardiness multi-objective genetic algorithms

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A novel case adaptation method based on an improved integrated genetic algorithm for power grid wind disaster emergencies

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第21期42卷 7812-7824页

作者： Zhang, Baishang Li, Xiangyang Wang, Shiying Harbin Inst Technol Sch Management Harbin 150001 Peoples R China

Case adaptation is a challenging and crucial process of Case-Based Reasoning (CBR) for power grid wind disaster emergencies. The statistical adaptation method is a traditional method that is independent of domain knowledge, is easy to implement, but is not proper for the complex system problem. Therefore, the aim of this paper is to propose a novel case adaptation method to address this problem by integrating the multi-objective genetic algorithm with gray relational analysis, called the grey relational analysis-multi-objective genetic algorithms method (GRAMOGA). Compared with the traditional method, GRAMOGA is performed in terms of corresponding relations between the case similarity and emergency plan, indicating a new idea for case adaptation. To improve adaptation accuracy, this paper improved the multi-objective genetic algorithm by using a selection method based on the fitness function. Furthermore, the frame theory is expanded by combining it with the D/S evidence theory, providing a novel method for case description and retrieval with incomplete information. A practical example from the south of Jiangsu demonstrates that GRAMOGA achieves better adaptation performance for power grid wind disaster emergencies. In addition to the practical applications in case adaptation, GRAMOGA can be used as a novel method for expanding the case base. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Case-Based Reasoning Case adaptation Grey relational analysis multi-objective genetic algorithms D/S evidence theory Wind disaster emergencies

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NSGA-DO: Non-Dominated Sorting genetic Algorithm Distance Oriented

NSGA-DO: Non-Dominated Sorting Genetic Algorithm Distance Or...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Pimenta, Adinovam H. M. Camargo, Heloisa de Arruda Univ Fed Sao Carlos Dept Comp Sci BR-13560 Sao Carlos SP Brazil

ISBN: (纸本)9781467374286

In this work, a multi-objective genetic algorithm named Non-dominated Sorting genetic Algorithm Distance Oriented (NSGA-DO) is proposed. It has been designed as a modification of the well known NSGA-II. The proposed algorithm is able to find non-dominated solutions that balance the Pareto front with respect to optimization of the objectives. The main characteristic of NSGA-DO is the distance oriented selection of solutions. At each iteration, the non-dominated solutions are used to find an approximation to the Pareto front. The algorithm uses the locations of the solutions in the approximated frontier to find the best distribution of solutions, which will guide the selection operations. In order to validate the proposal, NSGA-DO was applied in the context of multi-objective Evolutionary Fuzzy Systems (MOEFS), to the generation of fuzzy knowledge bases for classification. The study focus on the evaluation of the distribution of non-dominated solution as well as on the accuracy-interpretability trade-off. Experiments show the superiority of NSGA-DO when compared to NSGA-II in all three issues analyzed: dispersion of non-dominated solutions, accuracy and interpretability of the generated systems.

关键词： multi-objective genetic algorithms multi-objective Evolutionary Fuzzy Systems Automatic Generation of Fuzzy Rule Pareto-Optimal Front

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A genetic Type-2 Fuzzy Logic Based Approach for the Optimal Allocation of Mobile Field Engineers to their Working Areas

A Genetic Type-2 Fuzzy Logic Based Approach for the Optimal ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Starkey, Andrew Hagras, Hani Shakya, Sid Owusu, Gilbert Univ Essex Computat Intelligence Ctr Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England British Telecommun PLC Business Modelling & Operat Transformat Practice Ipswich Suffolk England

ISBN: (纸本)9781467374286

In utility based service industries with a large mobile workforce, there is a need to optimize the process of allocating engineers to tasks (i.e. fixing faults, installing new services, such as internet connections, gas or electricity etc.). Part of the process of optimizing the resource allocation to tasks involves finding the optimum area for an engineer to operate within, which we term as work area optimization. Work area optimization in large businesses can have a noticeable impact on business costs, revenues and customer satisfaction. However when attempting to optimize the workforce in real world scenarios, mostly single objective optimization algorithms are used while employing crisp logic. Nevertheless, there are many objectives that need to be satisfied and hence multi-objective based optimization will be more suitable. Even where multi-objective optimization is employed, the involved systems fail to recognize that these real world problems are full of uncertainties. Type-2 fuzzy logic systems can handle the high level of uncertainties associated with the dynamic and changing environments, such as those presented with real world scheduling problems. This paper presents a novel multi-objective genetic type-2 Fuzzy Logic based System for the optimal allocation of mobile workforces to their working areas. The method has been applied in a real world service industry workforce environment. The results show strong improvements when the proposed multi-objective type-2 fuzzy genetic based optimization system was applied to the work area optimization problem as compared to the heuristic or type-1 single objective optimization of the work area. Such optimization improvements of the working areas will result in improving the utilization of the workforce.

关键词： Type-2 fuzzy logic multi-objective genetic algorithms work area optimization

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Two Stage CMOS Operational Transconductance Amplifier for Front-End Electronics Design using multiobjective genetic algorithms 12

Two Stage CMOS Operational Transconductance Amplifier for Fr...

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International multi-Conference on Systems, Signals and Devices (SSD)

作者： Dendouga, Abdelghani Oussalah, Slimane Ctr Dev Technol Avancees Div Microelect & Nanotechnol Algiers Algeria

ISBN: (纸本)9781479917587

In this paper, we elaborate a program based on multi-objective genetic algorithms (MOGAs) to allow automated optimization of analog circuits. The proposed methodology is used to find the optimal transistors sizes (length and width) in order to obtain operational amplifier performances for analog and mixed CMOS-based circuit applications. Eight performances are considered in this study, direct current (DC) gain, unity-gain bandwidth (GBW), phase margin (PM), power consumption (P), area (A), slew rate (SR), thermal noise and signal to noise ratio (SNR). The program is solved using MATLAB Optimization Toolbox (TM) solvers. Also by using variables obtained from genetic algorithms, the operational transconductance amplifier (OTA) is simulated by using Cadence Virtuoso Spectre circuit simulator in standard TSMC (Taiwan Semiconductor Manufacturing Company) RF 0.18 mu m CMOS technology. A good agreement is observed between the program optimization and electric simulation.

关键词： CMOS analog circuit design multi-objective genetic algorithms optimization two stage OTA

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Tailor-made material design: An evolutionary approach using multi-objective genetic algorithms

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COMPUTATIONAL MATERIALS SCIENCE 2009年第1期45卷 1-7页

作者： Chakraborti, N. Sreevathsan, R. Jayakanth, R. Bhattacharya, B. Indian Inst Technol Dept Met & Mat Engn Kharagpur 721302 W Bengal India Indian Inst Technol Dept Civil Engn Kharagpur 721302 W Bengal India

Materials design may be defined as designing materials as dynamic multi level-structured systems with integrated and specific process/structure/performance/property relationships. The main objective of the work is to design structural materials based on inter-atomic potentials - the so-called "inverse problem" - to explore materials of high strength to weight ratio with a thermodynamically stable structure. Since the aforementioned objectives are contradicting each other it leads to a Pareto-optimal problem which is eventually solved by the multi-objective genetic algorithms solver NSGA-II. The material behavior is modeled using Lennard-Jones type interatomic potential function. The Pareto-optimal front provides a series of hypothetical materials which are then compared and contrasted with existing materials as and when possible. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Optimization Materials design Inter-atomic potential Stiffness Lightness Lennard-Jones potential multi-objective genetic algorithms NSGA-II

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