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2nd International Conference on Soft Computing for Problem Solving (SocProS)

作者： Singh, Sanjay Kr Katal, Nitish Modani, S. G. Amity Univ Dept ECE ASET Jaipur Rajasthan India Anand Int Coll Engn Dept ECE Jaipur Rajasthan India Malaviya Natl Inst Technol Jaipur Rajasthan India

ISBN: (纸本)9788132216025;9788132216018

The main aim of this chapter is to obtain optimal gains for a PID controller using multi-objective genetic algorithm used in a coupled-tank liquid-level control system. Liquid level control system is a nonlinear system and finds a wide application in petrochemical, food processing, and water treatment industries, and the quality of control directly affects the quality of products and safety. This chapter employs the use of multi-objective genetic algorithm for the optimization of the PID gains for better plant operations in contrast to conventional tuning methods and GA. The simulations indicate that better performance is obtained in case of multi-objective genetic algorithm-optimized PID controller.

关键词： PID controller multi-objective genetic algorithm PID optimization Liquid level control

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A genetic optimization of a hybrid organic Rankine plant for solar and low-grade energy sources

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ENERGY 2015年 91卷 807-815页

作者： Scardigno, Domenico Fanelli, Emanuele Viggiano, Annarita Braccio, Giacobbe Magi, Vinicio Univ Basilicata Sch Engn I-85100 Potenza Italy ENEA Tech Unit Trisaia Technol UTTri I-75026 Rotondella Italy

This paper provides a multi-objective optimization of a hybrid organic Rankine plant for solar and low-grade energy sources. In this plant, water, with a mass flow rate of 1 kg/s at a temperature of 90 degrees C, preheats the working fluid. The objective of this work is to search for solutions with the highest first and second law efficiencies of the plant and the lowest LEC (levelized energy cost). The design parameters are i) the working fluid, ii) the evaporating and condensing pressure, iii) the maximum temperature of the collector thermal fluid and iv) a parameter representative of the temperature profiles in the heat exchangers. A NSGAII (non-dominated sorting genetic algorithm) has been used. The Pareto front solutions provide Cyclopropane, R143a and R32 as working fluids. The lowest LEC (0.114 $/kWh) and the highest first law efficiency (9.65%) are achieved by using Cyclopropane, with a power output greater than 100 kW. The highest second law efficiency (44%) is obtained by employing R143a, with a low contribution of the solar source and a power output greater than 10 kW. Finally, R32 solutions have comparable performance with respect to R143a solutions. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Organic Rankine cycle multi-objective genetic algorithm Low-grade energy Solar energy Thermo-economic analysis

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Target Systems and Decision-making to Increase Production Sustainability

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Procedia CIRP 2016年 48卷 34-39页

作者： Matthias Putz Uwe Götze Johannes Stoldt Enrico Franz Fraunhofer Institute for Machine Tools and Forming Technology IWU Reichenhainer Straße 88 09126 Chemnitz Germany Chair of Management Accounting and Control Technische Universität Chemnitz Professur BWL III 09107 Chemnitz Germany

Many industrial companies are trying to improve their operation's sustainability. However, these efforts towards change are usually governed by economic considerations. This serves to neglect or at least diminish the perceivable relevance of ecologic or social consequences for investigated alternatives. This paper discusses the implications of multi-criterial target systems which extend the scope of considerations beyond economics to support the realisation of proactive environmental strategies. A special focus is set on the definition of targets as well as decision-making in brown-field planning projects. The findings are applied in a simulation-based study on the parameterisation of an energy-sensitive production control strategy.

关键词： Target systems Brown-field planning projects Sustainability Energy-sensitive production strategy multi-objective genetic algorithm NSGA-II

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Redesigning a Supply Chain Distribution Network: Formulation and genetic algorithm-Based Solution Procedure

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2015年第4期14卷 847-876页

作者： Zhou, Lina Xu, Xiaofei Deng, Shengchun Liu, Xudong Harbin Inst Technol Sch Software Harbin 150006 Heilongjiang Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Heilongjiang Peoples R China

Distribution affects company cost and customer satisfaction. Distribution network design is a strategic concern for nearly all companies, and must be periodically reviewed to ensure fitness to today's rapidly changing business environment. Unlike most of previous research, this paper investigates a highly complicated and realistic distribution network, and develops a multi-objective model to achieve an effective redesign solution. Reconfiguring cost, service differentiation, multiple products, and optional capacity levels are simultaneously considered. A determinant code-based encoding structure is designed to represent the complicated multi-objective optimization problem, and a genetic algorithm (GA)-based procedure is developed as a solution. The paper provides an application example using actual data. Finally, a comparison between the empirical results and those of the Prufer-based GA demonstrate the efficiency of the proposed GA.

关键词： Distribution network customer service level multi-objective genetic algorithm determinant code

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Proactive Project Scheduling in an R&D Department A Bi-objective genetic algorithm 5

Proactive Project Scheduling in an R&D Department A Bi-Objec...

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2015 International Conference on Industrial Engineering and Operations Management (IEOM)

作者： Capa, Canan Ulusoy, Gunduz Concordia Univ Mech & Ind Engn Montreal PQ Canada Sabanci Univ Mfg Syst Engn Istanbul Turkey

ISBN: (纸本)9781479960651

In this paper, we present part of a study on stochastic, dynamic project scheduling in an R&D Department of a leading home appliances company in Turkey. The problem under consideration is the preemptive resource constrained multi-project scheduling problem with generalized precedence relations in a stochastic and dynamic environment. The model consists of three phases. Phase I of the model provides a systematic approach to assess uncertainty resulting in activity deviation distributions. In Phase II, proactive project scheduling is accomplished through two different scheduling approaches, which employ a bi-objective genetic algorithm. Phase III is the reactive project scheduling phase aiming at rescheduling the disrupted project activities. Here, we will limit our presentation to Phase II-the proactive project scheduling phase. The procedure is demonstrated through an implementation with real data covering 37 R&D projects. Computational study is performed to compare the two different scheduling approaches called single and multi-project scheduling approaches, as well as two different chromosome evaluation heuristics. Results are presented and discussed.

关键词： Proactive project scheduling multi-objective genetic algorithm R&D

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genetic algorithm Optimized Grid-based RF Fingerprint Positioning in Heterogeneous Small Cell Networks

Genetic Algorithm Optimized Grid-based RF Fingerprint Positi...

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International Conference on Location and GNSS (ICL-GNSS)

作者： Mondal, Riaz Uddin Ristaniemi, Tapani Turkka, Jussi Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla Finland Magister Solut Ltd Tampere Finland

ISBN: (纸本)9781479998586

In this paper we propose a novel optimization algorithm for grid-based RF fingerprinting to improve user equipment (UE) positioning accuracy. For this purpose we have used multi-objective genetic algorithm (MOGA) which enables autonomous calibration of grid-cell layout (GCL) for better UE positioning as compared to that of the conventional fingerprinting approach. Performance evaluations were carried out using two different training data-sets consisting of Minimization of Drive Testing measurements obtained from a dynamic system simulation in a heterogeneous LTE small cell environment. The robustness of the proposed method has been tested analyzing positioning results from two different areas of interest. Optimization of GCL is performed in two ways: (1) array-wise calibration of the grid-cell units using non-overlapping GCL and (2) creating an overlapping GCL to cover of whole simulation area with different rectangular grid-cell units. Simulation results show that if sufficient amount of training data is available then the proposed method can improve positioning accuracy of 56.74% over the conventional grid-based RF fingerprinting.

关键词： Grid-based RF fingerprinting Minimization of Drive Tests multi-objective genetic algorithm Kullback-Leibler Divergence

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multi-Strategy genetic algorithm for Self-configuring Solving of Complex Optimization Problems 4

Multi-Strategy Genetic Algorithm for Self-configuring Solvin...

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IIAI 4th International Congress on Advanced Applied Informatics (IIAI-AAI)

作者： Sopov, Evgenii Siberian State Aerosp Univ SibSAU Syst Anal & Operat Res Dept Krasnoyarsk Russia

ISBN: (纸本)9781479999583

Many complex optimization problems require a modification of the general evolutionary algorithm (EA) according to the given features of the problem. There exist a great variety of EAs that represent different search strategies for many classes and subclasses of optimization problems. Real-world problems may combine several features that are not known beforehand, thus there is no information about what EA to choose and what EA's settings to apply for efficient problem solving. This study presents a novel metaheuristic for designing multi-strategy EA based on the hybrid of the island model, cooperative and competitive coevolution schemes. The approach controls interactions of EAs and leads to the self-configuring solving of problems with a priori unknown structure. Two examples of implementations of the approach for multi-objective and non-stationary optimization are discussed. The results of numerical experiments for benchmark problems from CEC competitions are presented. The proposed approach has demonstrated the efficiency comparable with other well-studied techniques for multi-objective and non-stationary optimization. And it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.

关键词： self-configuration multi-strategy evolutionary algorithm multi-objective genetic algorithm non-stationary optimization

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Time-Cost Trade-off Problem under Uncertainty Incorporating multi-objective genetic algorithm and Fuzzy Sets Theory

Time-Cost Trade-off Problem under Uncertainty Incorporating ...

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2010 IEEE 17th International Conference on Industrial Engineering and Engineering Management(2010年IEEE第17届工业工程与工程管理国际学术会议)

作者： C.H.Ding W.Wei X.Wei S.Fujimura Graduate School of Information Production and SystemsWaseda UniversityKitakyusyuJapan School of E Graduate School of Information Production and SystemsWaseda UniversityKitakyusyuJapan

The time-cost trade-off problem (TCTP) is an important branch in the project scheduling problem. However, the duration and cost of each activity could change stochastically as a result of uncertain factors. To meet the needs of real projects, an improved approach based on trapezoid fuzzy numbers is applied to estimate the uncertainty of time and cost. And then α-cut method is applied to decide the risk level. Furthermore, improved crossover and mutation methods for multiobjective genetic algorithm (MOGA) are used to make a large-scale computation possible. The efficiency of the proposed approach is verified by comparison with previous researches. In addition, economic analysis skill of finance cost is integrated into the new model to provide greater flexibility to managers when making decisions. Finally, time-cost tables under different risk levels for case examples are given and the advantages are investigated based on computation results.

关键词： Finance cost Fuzzy sets theory multi-objective genetic algorithm Project scheduling Risk management Time-cost trade-off problem

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Fuzzy delay model based fault simulator for crosstalk delay fault test generation in asynchronous sequential circuits

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2015年第1期40卷 107-119页

作者： Jayanthy, S. Bhuvaneswari, M. C. Sri Ramakrishna Engn Coll Dept Elect & Commun Engn ECE Coimbatore 641022 Tamil Nadu India PSG Coll Technol Dept Elect & Elect Engn EEE Coimbatore 641004 Tamil Nadu India

In this paper, a fuzzy delay model based crosstalk delay fault simulator is proposed. As design trends move towards nanometer technologies, more number of new parameters affects the delay of the component. Fuzzy delay models are ideal for modelling the uncertainty found in the design and manufacturing steps. The fault simulator based on fuzzy delay detects unstable states, oscillations and non-confluence of settling states in asynchronous sequential circuits. The fuzzy delay model based fault simulator is used to validate the test patterns produced by Elitist Non-dominated sorting genetic algorithm (ENGA) based test generator, for detecting crosstalk delay faults in asynchronous sequential circuits. The multi-objective genetic algorithm, ENGA targets two objectives of maximizing fault coverage and minimizing number of transitions. Experimental results are tabulated for SIS benchmark circuits for three gate delay models, namely unit delay model, rise/fall delay model and fuzzy delay model. Experimental results indicate that test validation using fuzzy delay model is more accurate than unit delay model and rise/fall delay model.

关键词： Asynchronous sequential circuits crosstalk delay faults fuzzy delay model multi-objective genetic algorithm fault coverage transitions

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Optimization of heat transfer enhancement of nanofluid in a channel with winglet vortex generator

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APPLIED THERMAL ENGINEERING 2015年 91卷 1116-1126页

作者： Abdollahi, Azita Shams, Mehrzad KN Toosi Univ Technol Fac Mech Engn Tehran Iran

A numerical simulation was performed to investigate the effect of the nanoparticles, vortex generator and combination of them on heat transfer and fluid flow characteristics in a rectangular channel using Commercial Computational Fluid Dynamics Code FLUENT. Euler-Lagrangian approach was utilized in the numerical modeling of the nanofluids. The fluid was considered as a continuous phase and the heat and flow fields were analyzed by solving Navier-Stokes and energy equations and the nanoparticles were simulated as a discrete phase in a Lagrangian frame. The thermal and hydraulic performances of the channel were investigated at different nanoparticle concentration, shape and angle of attack of the vortex generator. According to the results, the Nusselt number increases by raising the nanoparticles concentration and adding nanoparticles is more effective than placing VG from thermal point of view in the range of study. Using a combination of these heat transfer enhancers maximizes the thermal performance of the channel as well as the flow resistance. So, in order to achieve the best thermal-hydraulic performance, combination of the computational fluid dynamics analyses, artificial neural networks and multi-objective genetic algorithm was used to determine the optimal values of these parameters. Finally, a set of optimal solutions as well as the best shape, angle of attack of the VG and nanoparticles volume fraction was obtained. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Heat transfer enhancement Vortex generator Nanofluids multi-objective genetic algorithm Artificial neural network

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