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multiobjective planning of open-loop MV distribution networks using ComGIS network analysis and MOGA

3rd International Conference on Electric Utility Deregulation, Restructuring and Power Technologies

作者： Kong Tao Cheng Haozhong Hu Zechun Wang Chun Huang Wei Chen Chunqin Gao Yubo Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9787564112493

In this paper, an advanced method for the planning of open-loop medium voltage (MV) distribution network is proposed. The multiobjective planning model is constructed, and a special multiobjective genetic algorithm (MOGA) is designed based on the network dataset built by Component geographical information systems (ComGIS). The network analysis function of the ComGIS is embedded in the overall optimization process to find single-loop optimal paths. Crossover and mutation operator are designed according to characteristics of the coding. The evolutionary orientation is directed by the fitness function based on Pareto order of individuals. The Pareto optimal set (POS) including several candidate planning schemes is gotten through MOGA, from which the recommended scheme is selected. The method has been applied to real life power distribution networks, showing its potential in practical applications.

关键词： component geographical information systems distribution network planning medium-voltage distribution network multiobjective genetic algorithm network analysis open-loop configuration

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On maximizing solution diversity in a multiobjective multidisciplinary genetic algorithm for design optimization

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2004年第4期32卷 491-514页

作者： Gunawan, S Farhang-Mehr, A Azarm, S Univ Maryland Dept Mech Engn College Pk MD 20742 USA

This article presents a new method for multiobjective multidisciplinary design optimization. The method obtains an estimate of the Pareto frontier with maximum solution diversity using a quality index, referred to as entropy index. Unlike previous methods that maintain diversity in the solution set heuristically, our method improve, overall quality of solutions by explicitly optimizing the entropy index at every system-level iteration, and then using this information to bias the search process toward obtaining a solution set with maximum diversity. Our method utilizes a multiobjective genetic algorithm as an optimizer in each subproblem of a multidisciplinary optimization problem. To demonstrate the proposed approach, we applied our method to a mechanical design problem of a speed reducer and the results are compared with those obtained by a few other multiobjective optimization methods. A minimal set of quality indexes is used to compare the diversity and optimality of the obtained solution sets from the different methods on a quantitative basis.

关键词： multidisciplinary optimization multiobjective genetic algorithm solution diversity entropy

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genetic algorithms in optimization of strength and ductility of low-carbon steels

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MATERIALS AND MANUFACTURING PROCESSES 2007年第5-6期22卷 650-658页

作者： Ganguly, S. Datta, S. Chakraborti, N. Bengal Engn & Sci Univ Sch Mat Sci & Engn Sibpur 71103 Howrah India Indian Inst Technol Dept Met & Mat Engn Kharagpur 721302 W Bengal India

A comparative study between the conventional goal attainment strategy and an evolutionary approach using a genetic algorithm has been conducted for the multiobjective optimization of the strength and ductility of low-carbon ferrite-pearlite steels. The optimization is based upon the composition and microstructural relations of the mechanical properties suggested earlier through regression analyses. After finding that a genetic algorithm is more suitable for such a problem, Pareto fronts have been developed which give a range of strength and ductility useful in alloy design. An effort has been made to optimize the strength ductility balance of thermomechanically-processed high-strength multiphase steels. The objective functions are developed from empirical relations using regression and neural network modeling, which have the capacity to correlate high number of compositional and process variables, and works better than the conventional regression analyses.

关键词： acicularity artificial neural network design of steel ferrite-pearlite steel goal programming grain-refinement high-strength multiphase steel microstructure multiobjective genetic algorithm NSGA II pearlite content precipitates strength-ductility optimization thermomechanical processing

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multiobjective genetic Estimation to Induction Motor Parameters

Multiobjective Genetic Estimation to Induction Motor Paramet...

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2007 International Aegean Conference on Electrical Machines and Power Electronics, vol.1

作者： Tahir Sag Mehmet Cunkas Department of Electronics & Computer Education Faculty of Technical Education Selçuk University Konya Turkey

In order to simplify the offline identification of induction motor parameters, a method based on optimization using a multiobjective genetic algorithm is proposed. The non-dominated sorting genetic algorithm (NSGA-II) is used to minimize the error between the actual data and an estimated model. The robustness of the method is shown by identifying parameters of the induction motor in three different cases. The simulation results show that the method successfully estimates the motor parameters.

关键词： multiobjective genetic algorithm Induction motor Parameter estimation

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multiobjective OPTIMIZATION OF PROCESS PLANT USING genetic algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2006年第3期6卷 315-327页

作者： Govindarajan, L. Karunanithi, T. Annamalai Univ Dept Chem Engn Fac Engn & Technol Annamalainagar 608002 Tamil Nadu India

The optimal design of large-scale process plant is difficult due to the presence of Pareto sets or nondominated solutions. Many conventional and advanced mathematical techniques had been adopted which have their own limitations in solving the complex design problem. In this paper, nondominant-sorted genetic algorithms NSGA and NSGA-II have been adopted for the optimal design of complex Williams-Otto model process plant. The plant consists of a reactor, separation system consisting of heat exchanger, decanter and distillation column. multiobjective optimization is used to maximize the profit, i.e. the return on investment, to maintain lesser use of costlier raw material and lesser disposal of the waste by products. So NSGA-II is employed in this study as an effective replacement for NSGA, classical genetic algorithm, conventional and traditional methods of optimization in solving multiobjective process design problems and to achieve fine-tuning of variables in determining Pareto optimal design parameters. NSGA-II method finding global optimal front has a significant effect on the design of control system for the real time and continuous robust control of complex process plant as each target vector provides proper direction and drives the process to multiobjective optimum conditions.

关键词： multiobjective genetic algorithm NSGA NSGA-II optimization process plant

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Incorporating partial matches within multiobjective pharmacophore identification

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JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 2006年第12期20卷 735-749页

作者： Cottrell, Simon J. Gillet, Valerie J. Taylor, Robin Cambridge Crystallog Data Ctr Cambridge CB2 1EZ England Univ Sheffield Dept Informat Studies Sheffield S1 4DP S Yorkshire England

This paper describes the extension of our earlier multiobjective method for generating plausible pharmacophore hypotheses to incorporate partial matches. Diverse sets of molecules rarely adopt exactly the same binding mode, and so allowing the identification of partial matches allows our program to be applied to larger and more diverse datasets. The method explores the conformational space of a series of ligands simultaneously with their alignment using a multiobjective genetic algorithm (MOGA). The principles of Pareto ranking are used to evolve a diverse set of pharmacophore hypotheses that are optimised on conformational energy of the ligands, the goodness of the overlay and the volume of the overlay. A partial match is defined as a pharmacophoric feature that is present in at least two, but not all, of the ligands in the set. The number of ligands that map to a given pharmacophore point is taken into account when evaluating an overlay. The method is applied to a number of test cases extracted from the Protein Data Bank (PDB) where the true overlay is known.

关键词： pharmacophore molecular alignment MOGA multiobjective optimisation multiobjective genetic algorithm partial matches

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multiobjective OPTIMIZATION OF PROCESS PLANT USING genetic algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2005年第4期5卷 425-437页

作者： Govindarajan, L. Karunanithi, T. Annamalai Univ Fac Engn & Technol Dept Chem Engn Annamalainagar 608002 India

The optimal design of large-scale process plant is difficult due to the presence of Pareto sets or nondominated solutions. Many conventional and advanced mathematical techniques had been adopted which have their own limitations in solving the complex design problem. In this paper, nondominant-sorted genetic algorithms NSGA and NSGA-II have been adopted for the optimal design of complex Williams-Otto model process plant. The plant consists of a reactor, separation system consisting of heat exchanger, decanter and distillation column. multiobjective optimization is used to maximize the profit, i.e. the return on investment, to maintain lesser use of costlier raw material and lesser disposal of the waste byproducts. So NSGA-II is employed in this study as an effective replacement for NSGA, classical genetic algorithm, conventional and traditional methods of optimization in solving multiobjective process design problems and to achieve fine-tuning of variables in determining Pareto optimal design parameters. NSGA-II method finding global optimal front has a significant effect on the design of control system for the real time and continuous robust control of complex process plant as each target vector provides proper direction and drives the process to multiobjective optimum conditions.

关键词： multiobjective genetic algorithm NSGA NSGA-II optimization process plant

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Comparison study of SPEA2+, SPEA2, and NSGA-II in diesel engine emissions and fuel economy problem

Comparison study of SPEA2+, SPEA2, and NSGA-II in diesel eng...

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IEEE Congress on Evolutionary Computation

作者： Hiroyasu, T Nakayama, S Miki, M Doshisha Univ Dept Engn Kyoto 6100321 Japan

ISBN: (纸本)0780393635

Recently, the technology that can control NOx and Soot values of diesel engines by changing the electronically controllable parameters has been developed. However, there is a trade-off relationship between fuel economy and NOx values. Therefore, the diesel engines that can change their characteristics with along to the driving environment should be emerged in the future. For designing these kinds of engines, the Pareto solutions that can express the trade-off between fuel economy and NOx values are needed. In that case, the derived non dominated solutions should have the diversity not only in the objective space but also in the design variable space. SPEA2+ is one of multi objective genetic algorithms and is developed based on SPEA2. The derived non dominated solutions by SPEA2+ have the diversity in both objective space and design variable space. In this study, the diesel engines that have high fuel economy and small amounts of NOx and Soot are designed by SEPA2+. The results are compared with those of SPEA2 and NSGA-II. From the discussions, it is found that the solutions of SPEA2+ have the diversity not only in the objective space but also in the design variable space. These characteristics are very suitable for designing diesel engines whose parameters are changing against the driving environment.

关键词： air pollution control diesel engines fuel economy genetic algorithms nitrogen compounds NOx value NSGA-II Pareto solution SPEA2 SPEA2+ Soot value diesel engine emission electronically controllable parameter multiobjective genetic algorithm

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A fast optimization method of using nondominated sorting genetic algorithm (NSGA-II) and 1-nearest neighbor (1NN) classifier for numerical model calibration

A fast optimization method of using nondominated sorting gen...

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IEEE International Conference on Granular Computing

作者： Liu, Y Zhou, C Ye, WJ Univ Exeter Dept Engn Exeter EX4 4QF Devon England

ISBN: (纸本)0780390172

Practical experience with numerical model calibration suggests that no single objective is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. The multi-objective genetic algorithm (MOGA) is used as automatic calibration method for a wide range of numerical models. The task of estimating the entire Pareto set requires a large number of fitness evaluations in a standard MOGA optimization process. However, it's very time consuming to obtain a value of objective functions in many real-world engineering problems. The NSGA-II-1NN algorithm, an effective and efficient methodology to reduce the number of actual fitness evaluations for solving the multiple-objective global optimization problem, is presented in this paper. The test results for multi-objective calibration show that the proposed method only requires about 38 percent of actual fitness evaluations of the NSGA-II.

关键词： Pareto optimisation calibration genetic algorithms pattern classification 1-nearest neighbor classifier MOGA optimization process NSGA-II-1NN algorithm Pareto set automatic calibration method fitness evaluation multiobjective genetic algorithm multiple-o genetic algorithm (GA) Pareto optimisation Pattern recognition CALIBRATION METHODS optimization method health

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A new efficient and useful robust optimization approach - Design for multi-objective six sigma

A new efficient and useful robust optimization approach - De...

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IEEE Congress on Evolutionary Computation

作者： Shimoyama, K Oyama, A Fujii, K Univ Tokyo Dept Aeronaut & Astronaut Sagamihara Kanagawa 2298510 Japan

ISBN: (纸本)0780393635

An efficient and useful robust optimization approach, design for multi-objective six sigma (DFMOSS), has been developed. The DFMOSS couples the ideas of design for six sigma (DFSS) and multi-objective genetic algorithm (MOGA) to solve drawbacks of DFSS;DFMOSS obtains trade-off solutions between optimality and robustness in one optimization. In addition, it does not need careful parameter tuning. Robust optimizations of a test function and welded beam design problem demonstrated that DFMOSS is more effective and more useful than DFSS.

关键词： design engineering genetic algorithms six sigma (quality) welding design for multiobjective six sigma multiobjective genetic algorithm optimization test function welded beam design problem

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