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Multi-objective genetic algorithm on hexagonal search for fast motion estimation

IET IMAGE PROCESSING 2019年第11期13卷 1892-1902页

作者： Palaniraj, Palanivel Sakthivel, Ganesan Annamalai Univ Dept Elect & Instrumentat Engn Annamalainagar 608002 Tamil Nadu India

Motion estimation (ME) is a process of correlating the frames by pointing the blocks in the candidate frame similar to those in the previous frame. Hexagonal Search (HS) reduces 10-15% search points over Diamond Search (DS) with negligible Peak Signal to Noise Ratio (PSNR) loss. Adapting an optimization will achieve a massive improvement on the search process. The proposed HS-GA method combines the HS with genetic algorithm (GA) to optimize ME. The HS-GA method reduces 72% on average search points pre block (ASPB) with PSNR loss of 2.5 dB and single generation. If the GA is executed with 10 generations the PSNR loss is reduced to 0.48 dB, but ASPB drastically increase to similar to 316%. The proposed work aims at multi objective on selection of better chromosomes and on control of generations to avoid the unnecessary generations to improve the GA. This process is named HSIGA(HS with Intelligent GA). The proposed HSIGA adapts five intelligent techniques like Histogram to decide new generations, Region based chromosome selection, Mutation on inner search gene, second rank valley point stopping criteria and interlaced HS to improve search process with negligible PSNR loss with in affordable search.

关键词： search problems genetic algorithms probability motion estimation cellular biophysics Poisson distribution negligible PSNR loss optimisation search process HS-GA method gene selection domain Poisson-distributed random genes ASPB region-based chromosome selection inner search gene multiobjective genetic algorithm hexagonal search fast motion estimation diamond search average search points pre-block second-rank valley point stopping criteria

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Incremental multiple objective genetic algorithms

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2004年第3期34卷 1325-1334页

作者： Chen, Q Guan, SU Natl Univ Singapore Dept Elect & Comp Engn Singapore 119260 Singapore

This paper presents a new genetic algorithm approach to multiobjective optimization problems-incremental multiple objective genetic algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages. First, an independent population is evolved to optimize one specific objective. Second, the better-performing individuals from the single-objecive population evolved in the above stage and the multiobjective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multiobjective population, to which a multiobjective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA, and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better.

关键词： incremental problem solving multiobjective genetic algorithm multiobjective problems multiobjective optimization vector optimization

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Sensitivity and specificity based multiobjective approach for feature selection: Application to cancer diagnosis

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INFORMATION PROCESSING LETTERS 2009年第16期109卷 887-896页

作者： Garcia-Nieto, J. Alba, E. Jourdan, L. Talbi, E. Univ Malaga Dept Lenguajes & Ciencias Computac ETSI Informat E-29071 Malaga Spain LIFL INRIA Futurs F-59650 Villeneuve Dascq France

The study of the sensitivity and the specificity of a classification test constitute a powerful kind of analysis since it provides specialists with very detailed information useful for cancer diagnosis. In this work, we propose the use of a multiobjective genetic algorithm for gene selection of Microarray datasets. This algorithm performs gene selection from the point of view of the sensitivity and the specificity. both used as quality indicators of the classification test applied to the previously selected genes. In this algorithm, the classification task is accomplished by Support Vector Machines: in addition a 10-Fold Cross-Validation is applied to the resulting subsets. The emerging behavior of all these techniques used together is noticeable, since this approach is able to offer, in an original and easy way. a wide range of accurate solutions to professionals in this area. The effectiveness of this approach is proved on public cancer datasets by working out new and promising results. A comparative analysis Of Our approach using two and three objectives, and with other existing algorithms. suggest that our proposal is highly appropriate for solving this problem. (C) 2009 Elsevier B.V. All rights reserved.

关键词： algorithms Analysis of algorithms Combinatorial problems Databases Design of algorithms Performance evaluation Sensitivity and specificity analysis multiobjective genetic algorithm Microarray gene selection

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Optimization of the Low-Impact Development Facility Area Based on a Surrogate Model

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WATER RESOURCES MANAGEMENT 2023年第14期37卷 5789-5806页

作者： Feng, Jing Yang, Yuanyuan Li, Jianzhu Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300350 Peoples R China Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Peoples R China

Low-impact development (LID) facilities constitute an important element of sponge cities. In this paper, a system of siting suitability indicators was established for green roofs, permeable pavement and rain gardens. Based on the ArcGIS superposition analysis tool, the suitable area and area upper limit of LID facilities in each subcatchment were determined. Nonlinear functions of the LID deployment area and various rainfall characteristics were constructed using a multilayer perceptron (MLP) as a surrogate model. The results showed that (1) the upper limit of the suitable area for green roof deployment in each subcatchment ranged from 5.46% to 22.87%. The upper limit of the suitable area for permeable pavement application varied between 8.31% and 23.65%. The upper limit of the suitable area for rain garden deployment varied between 18.12% and 36.95%;(2) a final LID facility layout scheme was selected at a total outflow of 10,700 m3, peak discharge of 3.49 m3/s and total investment of 0.76 billion yuan during the construction and management period;(3) the total outflow reduction rate under the LID model ranged from 32.15% to 40.29%, and the peak discharge reduction rate ranged from 17.93% to 36.20%;(4) the computation time of the surrogate optimization model could be reduced to 0.37% of that of Storm Water Management Model (SWMM) conventional numerical optimisation. Herein, the surrogate model can be used to determine the optimal solution for the LID facility placement area assuming fixed reduction amounts of the outflow and peak discharge.

关键词： LID facilities Multilayer perceptron multiobjective genetic algorithm Analytic hierarchy process SWMM

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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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Functional optimization of a Persian lime packing using TRIZ and multi-objective genetic algorithms

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 139卷 105558-000页

作者： Alfonso Aguilar-Lasserre, Alberto Eduardo Torres-Sanchez, Victor Fernandez-Lambert, Gregorio Azzaro-Pantel, Catherine Cortes-Robles, Guillermo Alberto Roman-del Valle, Missael Tecnol Nacl Mexico Inst Tecnol Nacl Mexico Inst Tecnol Orizaba Div Estudios Posgrad & Invest Av Oriente 9852 Orizaba 94320 Veracruz Mexico Inst Tecnol Super Misantla Div Estudios Posgrad & Invest Km 1-8Carretera Loma del Cojolite S-N Misantla 93821 Veracruz Mexico Univ Toulouse Lab Genie Chim UMR 5503 CNRSINPENSIAGET 4 Allee Emile MonsoBP 84234 F-3143 Toulouse France

This article proposes a novel approach that uses a mathematical model optimized by genetic algorithms harmonized with the Russian theory of problem solving and invention (TRIZ) to design an export packing of Persian Lime. The mathematical model (with functional elements of non-spatial type) optimizes the spaces of the Persian Lime Packing, maximizes the Resistance to Vertical Compression and minimizes the Amount of Material Used, according to the operation restrictions of the packing during the transport of the merchandise. This approach is developed in four phases: the identification of the solution space;the optimization of the conceptual design;the application of TRIZ;and the generation of the final proposal solution. The results show the proposed packing (with 28% less cardboard) supports at least the same vertical load with respect to the nearest competitor packing. However, with the same number of packings per pallet and pallets per container, the space used by the packing assembled and deployed in the container is greater by 10% and 38% respectively. Besides, TRIZ includes innovative non-spatial elements such as the airflow and the friction of the product inside the packing. The contribution of this approach can be replicable for the packing design of other horticultural products of the agri-food chain.

关键词： multiobjective genetic algorithm TRIZ Contradiction matrix Packing and packaging Fresh fruit exportation Packaging innovation

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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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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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Multi-objective optimisation of wavelet features for phoneme recognition

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IET SIGNAL PROCESSING 2016年第6期10卷 685-691页

作者： Daniel Vignolo, Leandro Leonardo Rufiner, Hugo Humberto Milone, Diego FICH UNL CONICET Sinci Res Inst Signals Syst & Computat Intelligence Ciudad Univ UNLS3000 Santa Fe Argentina Lab Cibernet FI UNER E3100 Oro Verde Entre Rios Argentina

State-of-the-art speech representations provide acceptable recognition results under optimal conditions, though their performance in adverse conditions still needs to be improved. In this direction, many advances involving wavelet processing have been reported, showing significant improvements in classification performance for different kinds of signals. However, for speech signals, the problem of finding a convenient wavelet-based representation is still an open challenge. This study proposes the use of a multi-objective genetic algorithm for the optimisation of a wavelet-based representation of speech. The most relevant features are selected from a complete wavelet packet decomposition in order to maximise phoneme classification performance. Classification results for English phonemes, in different noise conditions, show significant improvements compared with well-known speech representations.

关键词： speech recognition wavelet transforms genetic algorithms signal representation signal classification multiobjective optimisation phoneme recognition signal classification performance speech signals multiobjective genetic algorithm wavelet-based speech representation wavelet packet decomposition feature selection English phonemes noise conditions

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Hierarchical component-based representations for evolving microelectromechanical systems designs

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 2011年第1期25卷 41-55页

作者： Zhang, Ying Agogino, Alice M. Georgia Inst Technol Sch Elect & Comp Engn Savannah GA 31407 USA Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

In this paper we present a genotype representation method for improving the performance of genetic-algorithm-based optimal design and synthesis of microelectromechanical systems. The genetic algorithm uses a hierarchical component-based genotype representation, which incorporates specific engineering knowledge into the design optimization process. Each microelectromechanical system component is represented by a gene with its own parameters defining its geometry and the way it can be modified from one generation to the next. The object-oriented genotype structures efficiently describe the hierarchical nature typical of engineering designs. They also encode knowledge-based constraints that prevent the genetic algorithm from wasting time exploring inappropriate regions of the search space. The efficiency of the hierarchical component-based genotype representation is demonstrated with surface-micromachined resonator designs.

关键词： Component-Based Genotype Representation Microelectromechanical System Design multiobjective genetic algorithm

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