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multi-objective parametric optimization on machining with wire electric discharge machining

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第5-8期62卷 617-633页

作者： Kumar, Kapil Agarwal, Sanjay Bundelkhand Inst Engn & Technol Dept Mech Engn Jhansi 284128 Uttar Pradesh India

The selection of optimum machining conditions, during wire electric discharge machining process, is of great concern in manufacturing industries these days. The increasing quality demands, at higher productivity levels, require the wire electric discharge machining process to be executed more efficiently. Specifically, the material removal rate needs to be maximized while controlling the surface quality. Despite extensive research on wire electric discharge machining process, determining the desirable operating conditions in industrial setting still relies on the skill of the operators and trial-and-error methods. In the present work, an attempt has been made to optimize the machining conditions for maximum material removal rate and maximum surface finish based on multi-objective genetic algorithm. Experiments, based on Taguchi's parameter design, were carried out to study the effect of various parameters, viz. pulse peak current, pulse-on time, pulse-off time, wire feed, wire tension and flushing pressure, on the material removal rate and surface finish. It has been observed that a combination of factors for optimization of each performance measure is different. So, mathematical models were developed between machining parameters and responses like metal removal rate and surface finish by using nonlinear regression analysis. These mathematical models were then optimized by using multi-objective optimisation technique based on Non-dominated Sorting genetic algorithm-II to obtain a Pareto-optimal solution set.

关键词： WEDM Taguchi technique multi-objective genetic algorithm

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multi-objective genetic algorithm for multistage-based job processing schedules in FMS environment

Multi-objective genetic algorithm for multistage-based job p...

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IEEE International Conference on Control and Automation

作者： Kim, KwanWoo Lee, DongJoo Jeong, In-Jae Hanyang Univ Dept Ind Engn Seoul 133791 South Korea

ISBN: (纸本)9781424408177

In this paper, we propose a multi-objective genetic algorithm for effectively solving multistage-based job processing schedules in FMS environment. The proposed method is random-weight approach to obtaining a variable search direction toward Pareto solution. The objectives are to minimize the makespan and the total flow time, simultaneously. The feasibility and adaptability of the proposed moGA are investigated through experimental results.

关键词： multistage-based job processing schedule multi-objective genetic algorithm FMS

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multi-objective genetic algorithm to mitigate the composite problem of total transfer capacity, voltage stability and transmission loss minimization

Multi-objective genetic algorithm to mitigate the composite ...

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39th North American Power Symposium

作者： Ramana, N. Venkata Chandrasekar, K. JNT Univ Coll Engn Dept Elect & Elect Engn Kakinada India JNT Univ Coll Engn Dept Elect & Elect Engn Hyderabad Andhra Pradesh India

ISBN: (纸本)9781424417254

Enhancement in total transfer capacity, Voltage stability and minimization of transmission tine losses are treated as an interrelated, coupled composite problem. A multi-objective genetic algorithm is presented to mitigate this multi-objective and multi-criteria type composite problem. Simulation test is carried on the standard EEEE WSCC 3-Generator, 9-Bus system. The results indicate significant improvement in the three problems as well as economical justification is achieved through the application of the algorithm. The algorithm suggests using both SVC and TCSC at their optimal locations has significant impact in dealing the problem.

关键词： total transfer capacity multi-objective genetic algorithm TCSC SVC repeated power flow

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Optimal Short-term Reservoir Operation with Integrated Long-term Goals

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WATER RESOURCES MANAGEMENT 2012年第10期26卷 2833-2850页

作者： Sreekanth, J. Datta, Bithin Mohapatra, Pranab K. CSIRO Land & Water Ecosci Precinct Dutton Pk Qld 4102 Australia James Cook Univ Townsville Qld 4811 Australia IIT Kanpur Dept Civil Engn Kanpur 208016 Uttar Pradesh India

A methodology to incorporate long-term goals within the short-term reservoir operation optimization model is proposed. Two conflicting objectives for the management of hydropower generation in two different power plants are incorporated. A chance-constrained optimization model is used to derive long-term (annual) operation strategies. With the time horizon of operation for the short-term optimization model kept equal to a single time-step of the long-term optimization model, the optimum end storages derived from the long-term model are incorporated as constraints (storage lower bounds) within the short-term model. The long-term benefits accrued from such an operation model are illustrated for a small reservoir, in South India. The solutions are compared with the historic operation. These are also compared with the solutions of a short-term optimal operation model ignoring long-term goals. The optimization model is solved using a multi-objective genetic algorithm.

关键词： Reservoir operation Short-term, long-term optimization Hydro-power multi-objective genetic algorithm

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Characterising the dynamics of EEG waveforms as the path through parameter space of a neural mass model: Application to epilepsy seizure evolution

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NEUROIMAGE 2012年第3期59卷 2374-2392页

作者： Nevado-Holgado, Alejo J. Marten, Frank Richardson, Mark P. Terry, John R. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN S Yorkshire England Univ Bristol Fac Engn Bristol BS8 1TR Avon England Kings Coll London Inst Psychiat London SE5 8AF England Univ Sheffield Sheffield Inst Translat Neurosci Sheffield S10 2HQ S Yorkshire England

In this paper we propose that the dynamic evolution of EEG activity during epileptic seizures may be characterised as a path through parameter space of a neural mass model, reflecting gradual changes in underlying physiological mechanisms. Previous theoretical studies have shown how boundaries in parameter space of the model (so-called bifurcations) correspond to transitions in EEG waveforms between apparently normal, spike and wave and subsequently poly-spike and wave activity. In the present manuscript, we develop a multi-objective genetic algorithm that can estimate parameters of an underlying model from clinical data recordings. A standard approach to this problem is to transform both clinical data and model output into the frequency domain and then choose parameters that minimise the difference in their respective power spectra. Instead in the present manuscript, we estimate parameters in the time domain, their choice being determined according to the best fit obtained between the model output and specific features of the observed EEG waveform. This results in an approximate path through the bifurcation plane of the model obtained from clinical data. We present comparisons of such paths through parameter space from separate seizures from an individual subject, as well as between different subjects. Differences in the path reflect subtleties of variation in the dynamics of EEG, which at present appear indistinguishable using standard clinical techniques. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Neural mass model Nonlinear parameter estimation multi-objective genetic algorithm Time-domain estimation Bifurcation analysis Nonlinear dynamics

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REDUCTION OF ENERGY LOSSES IN ELECTRICAL DISTRIBUTION SYSTEMS

REDUCTION OF ENERGY LOSSES IN ELECTRICAL DISTRIBUTION SYSTEM...

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International Conference and Exhibition on Electricity Distribution

作者： Hamed EMARA KASSEM Mohammed BADR Salwa ALI AHMED EX-Chairman of Rural Electrification Authority Faculty of Engineering Ain Shams university Egyptian Electricity Holding Company (EEHC)

ISBN: (纸本)9781629931333

This paper contains a strategy to minimize energy losses in an electrical distribution network. In order to develop the strategy, a model was constructed to simulate an electrical distribution network, and different parameters were included that help in estimation of technical losses in medium voltage (MV) distribution network. The main objective of this paper is to minimize technical and non-technical losses in power systems. There are losses in power systems that cannot be predicted or calculated beforehand, these are the non-technical losses. The bulk of these losses are likely caused by electricity theft, rather than the other possibilities such as poor maintenance of meters, and calculation and accounting mistakes, some power systems may suffer from both. The strategy is applied on a part of real distribution network. The network segments consist of different network busses delivering electrical energy from substation to supply different customers. In this network, the load supplied by one bus is a varying load for different times especially at maximum load conditions.

关键词： Technical losses Non-technical losses Maneuvers PRAO software multi-objective genetic algorithm Electrical distribution network

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multi-point robust design optimization of wind turbine airfoil under geometric uncertainty

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2012年第A2期226卷 245-261页

作者： Ju, Y. P. Zhang, C. H. Xi An Jiao Tong Univ Dept Fluid Machinery & Engn Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China

Modern wind turbine airfoil designs are increasingly emphasizing low sensitivity to the leading edge roughness in addition to good aerodynamic performances under varying wind conditions. In this study, a multi-point robust design optimization method has been systematically established for the wind turbine airfoil. The objective is to not only maximize the lift-to-drag ratio and lift coefficient at and near the design point, but also minimize their sensitivity to the leading edge roughness associated with the geometry profile uncertainty. The geometry parameterization of the airfoil is conducted by the Bezier curves. In the robust optimization, the multi-objective genetic algorithm, Monte Carlo simulation technique, and artificial neural network model are used. The results show that both the robust Pareto optimal (RPO) and deterministic Pareto optimal (DPO) airfoils have higher lift-to-drag ratio and lift coefficient than the original design FX 63-137 at and near the design point. A smaller maximum camber and larger radii near the leading edge help the RPO airfoil outperform both the DPO and original ones in terms of low sensitivity to the leading edge roughness. This study may be useful to the future development of the wind turbine airfoils with higher efficiency and reliability.

关键词： wind turbine airfoil leading edge roughness robust design optimization multi-objective genetic algorithm Monte Carlo simulation artificial neural network

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Large System multi-objective Model of Optimal Allocation for Water Resources in Jiansanjiang Branch Bureau

Large System Multi-objective Model of Optimal Allocation for...

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5th International Conference on Computer and Computing Technologies in Agriculture (CCTA)

作者： Lv, Ping Liu, Dong NE Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Heilongjiang Peoples R China

ISBN: (纸本)9783642272806

According to the imbalance development and utilization of water resources, water shortages and other issues in Sanjiang Plain, taking Jiansanjiang branch bureau as an example, the multi-objective optimal allocation model of water resources is established with goal of maximum economic and social benefits. Only surface water, groundwater and transit water are considered overall and different water demands in industry, life and agriculture are satisfied can we realize the rational allocation of regional water resources. The large system decomposition-coordination theory and multi-objective genetic algorithm are applied to solve the model. The optimization results showed that, the water shortage situation in Jiansanjiang branch bureau is improved in planning years and surface water supply capacity can be increased gradually and groundwater resources can be effectively protected. The optimal allocation model and solution method are effective and feasible, and the optimal allocation results are reasonable. The research can provide scientific basis for rational development and utilization of water resources in Jiansanjiang branch bureau and Sanjiang Plain.

关键词： Jiansanjiang branch bureau optimal allocation of water resources multi-objective genetic algorithm large system decomposition-coordination

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Space Active Noise Control System Design with multi-objective genetic algorithms

Space Active Noise Control System Design with Multi-objectiv...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Liu Huideng Qiu ARui Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781424478354

The model for the space active noise control system is investigated in this paper and is converted to a multi-objective optimization problem with constraints, of which the positions of secondary speakers and error sensors are the decision variables, the summation of the squared pressure at all points within the noise quiet zone and the total source strength for the secondary speakers are the multi-objective functions. The multi-objective genetic algorithms and simple genetic algorithm are implemented to solve the optimization problem so as to determine the appropriate positions of the secondary speakers and error sensors. The large sound pressure reductions within the noise quiet zone to control the single tone primary noise and motor operating noise show that the optimal schemes obtained by the multi-objective genetic algorithms are efficient.

关键词： Active noise control(ANC) space multi-objective genetic algorithm

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MOGA for multi-Level Fuzzy Data Mining

MOGA for Multi-Level Fuzzy Data Mining

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IEEE International Conference on Granular Computing (GrC)

作者： Chen, Chun-Hao Ho, Chi-Hsuan Hong, Tzung-Pei Lin, Wei-Tee Tamkang Univ Dept Comp Sci & Infonnat Engn Taipei Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung Taiwan Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781467323109;9781467323116

In this paper, we propose a multi-objective multi-Level genetic-Fuzzy Mining (MOMLGFM) algorithm for mining a set of non-dominated membership functions for mining multi-level fuzzy association rules. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The two objective functions of each chromosome are then calculated. The first one is the summation of large 1-itemsets of each item in different concept levels, and the second one is the suitability of membership functions. The fitness value of each individual is then evaluated by these two objective functions. After the GA process terminates, various sets of membership functions could be used for deriving multiple-level fuzzy association rules according to decision maker. Experimental results on a simulation dataset also show the effectiveness of the algorithm.

关键词： data mining multi-objective genetic algorithm multi-concept levels membership function fuzzy association rule

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