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IEEE Congress on Evolutionary Computation (CEC)

作者： Ghaffarizadeh, Ahmadreza Ahmadi, Kamilia Flann, Nicholas S. Utah State Univ Dept Comp Sci Logan UT 84322 USA

ISBN: (纸本)9781424478354

Sorting by reversals is a simplified version of the genome rearrangement problem that seeks to discover the evolutionary relationship between different genomes, and is one of the many challenging problems in Bioinformatics. Solving the problem optimally has been proved to be NP-Hard and so a selection of approximation algorithms have been developed. In this paper a new mapping order is introduced to solve the problem of sorting unsigned permutations using a specialized multi-objective genetic algorithm. Our modified genetic algorithm uses a population with variable length individuals to maintain a worst time running time complexity of O(n(4) log(2) n), where n is the problem size. The results show that this approach is more effective than the 3/2 heuristic method and previous genetic algorithm approaches.

关键词： Sorting by reversals genome rearrangement sorting unsigned permutations variable size individuals multi-objective genetic algorithm

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Evolutionary multi-objective Design Optimisation of Energy Harvesting MEMS The Case of a Piezoelectric

Evolutionary Multi-objective Design Optimisation of Energy H...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Benkhelifa, Elhadj Moniri, Mansour Tiwari, Ashutosh de Rueda, Alfonso G. Staffordshire Univ Fac Comp Engn & Technol Stafford England Cranfield Univ Decis Engn Cent Mfg Dept Cranfield MK43 0AL Beds England

ISBN: (纸本)9781424478354

The design and optimisation of Energy Harvesting (EH) Micro-Electromechanical-Systems (MEMS) is of particular interest in this research. The application of such devices is becoming an attractive alternative to the traditional use of batteries in wireless and body sensor networks. An evolutionary multi-objective Design Optimisation (DO) Framework is developed to experiment with one class of EH-MEMS, namely, Piezoelectric, using a reconstructed analytical model of the system. The application of such a Framework in this application domain is unprecedented and has already shown very promising results and in some cases it outperformed the human engineer. A thorough analysis of the results has been undertaken, which reveals interesting conclusions about the behaviour and physics of such devices. Besides, the main features of the Framework are explored enabling the enhancement of the MEMS-DO.

关键词： multi-objective genetic algorithm design optimisation Energy Harvesting MEMS evolutionary computing Piezoelectric mobility

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Classifier Ensembles Optimization Guided by Population Oracle

Classifier Ensembles Optimization Guided by Population Oracl...

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IEEE Congress on Evolutionary Computation (CEC)

作者： dos Santos, Eulanda M. Sabourin, Robert Univ Fed Amazonas Manaus Amazonas Brazil Ecole Technol Super Montreal PQ Canada

ISBN: (纸本)9781424478354

Dynamic classifier ensemble selection is focused on selecting the most confident classifier ensemble to predict the class of a particular test pattern. The overproduce-and-choose strategy is a dynamic classifier ensemble selection method which is divided into optimization and dynamic selection phases. The first phase involves the test of different candidate ensembles in order to produce a population composed of the highest performing candidate ensembles. Then, the second phase calculates the domain of expertise of each candidate ensemble to pick up the solution with highest degree of certainty of its decision to classify the unknown test samples. It has been shown that the optimization phase decreases oracle, the upper bound of dynamic selection processes. In this paper we propose a hybrid algorithm to perform the optimization phase of overproduce-and-choose strategy. The proposed algorithm combines stochastic initialization of candidate ensembles of different sizes, with the traditional forward search greedy method. The objective is to apply oracle as search criterion during the optimization phase. We show experimentally that choosing the population of classifier ensembles taking into account the population oracle leads to increase the upper bound of the dynamic selection phase. Moreover, experimental results conducted to compare the proposed method to a multi-objective genetic algorithm (MOGA), demonstrate that our method outperforms MOGA on generating population of candidate ensembles with higher oracle rates.

关键词： Dynamic classifier ensemble selection hybrid search algorithm multi-objective genetic algorithm pattern recognition

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multi-objective Optimization for High Strength Steel Sheet Metal Forming Process Based on Response Surface Methodology

Multi-objective Optimization for High Strength Steel Sheet M...

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International Conference on Advances in Materials and Manufacturing Processes

作者： An, Zhiguo Zhang, Yu Chongqing Jiaotong Univ Coll Mech Elect & Automobile Engn Chongqing 400074 Peoples R China

ISBN: (纸本)9780878492046

In sheet metal forming process, the input process parameters scatter and considerably result in unreliablity in practical production. Optimization for sheet metal forming process is often considered as a multi-objective problem. An optimizition strategy for high strength steel (HSS) sheet metal forming process was suggested based on response surface methodology (RSM). Latin Hypercube Sampling (LHS) method was introduced to design the rational experimental samples;the objective function was defined based on cracking factor wrinkle factor and severe thinning factor;the accurate response surface for sheet metal forming problem was built by Least Square Method;multi-objective genetic algorithm(MOGA) was adoped in optimization and Pareto solution was selected. The strategy was applied to analyze a HSS auto-part, the result has proved this method suitable for optimization design of HSS sheet metal forming process.

关键词： Response Surface Methodology Design of Experiment Sheet Metal Forming Latin Hypercube Sampling multi-objective genetic algorithm

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multi-objective genetic algorithm for solving N-version program design problem

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2006年第9期91卷 1083-1094页

作者： Yamachi, Hidemi Tsujimura, Yasuhiro Kambayashi, Yasushi Yamamoto, Hisashi Nippon Inst Technol Dept Comp & Informat Engn Miyashiro Saitama 3458501 Japan Tokyo Metropolitan Inst Technol Dept Prod & Informat Syst Engn Tokyo 1910065 Japan

N-version programming (N-VP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0-1 nonlinear integer programming problem. In order to overcome this problem, we propose a multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost. (C) 2005 Elsevier Ltd. All

关键词： fault tolerant software N-version program multi-objective genetic algorithm Pareto solutions distance-based selection

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multi-objective genetic algorithm-based sample selection for partial least squares model building with applications to near-infrared spectroscopic data

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APPLIED SPECTROSCOPY 2006年第6期60卷 631-640页

作者： Shinzawa, Hideyuki Li, Boyan Nakagawa, Takehiro Maruo, Katsuhiko Ozaki, Yukihiro Matsushita Elect Works Ltd NBT Project Kadoma Osaka 5718686 Japan

In this study, multi-objective genetic algorithms (GAs) are introduced to partial least squares (PLS) model building. This method aims to improve the performance and robustness of the PLS model by removing samples with systematic errors, including outliers, from the original data. multi-objective GA optimizes the combination of these samples to be removed. Training and validation sets were used to reduce the undesirable effects of over-fitting on the training set by multi-objective GA. The reduction of the over-fitting leads to accurate and robust PLS models. To clearly visualize the factors of the systematic errors, an index defined with the original PLS model and a specific Pareto-optimal solution is also introduced. This method is applied to three kinds of near-infrared (NIR) spectra to build PLS models. The results demonstrate that multi-objective GA significantly improves the performance of the PLS models. They also show that the sample selection by multi-objective GA enhances the ability of the PLS models to detect samples with systematic errors.

关键词： partial least squares PLS multi-objective genetic algorithm GA sample selection least median squares LMedS systematic error near-infrared spectroscopy NIR spectroscopy

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multi-objective genetic algorithm for hybrid electric vehicle parameter optimization

Multi-objective genetic algorithm for hybrid electric vehicl...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Huang, Bufu Wang, Zhancheng Xu, Yangsheng Chinese Univ Hong Kong Dept Automat & Comp Aided Engn Shatin Hong Kong Peoples R China

ISBN: (纸本)9781424402588

As a typical multi-objective optimization problem, parameter optimization of HEV power control strategy must deal with the conflict between objectives, as fuel consumption and emissions. Classical methods define the HEV parameter optimization as a single objective problem to minimize the fuel consumption. In this paper, the multi-objective genetic algorithm (MOGA) is generalized for parameter optimization of power control strategy of series hybrid electric vehicle. Using a single unified formulation, a number of design objectives can be simultaneously optimized through searching in the parameter space. Compared with two main strategies, as Thermostatic and single-objective genetic algorithm (SOGA), the computation procedures of MOGA are discussed. Simulation results based on the model of series hybrid electric vehicle illustrate the optimization validity of MOGA.

关键词： multi-objective genetic algorithm power control strategy series hybrid electric vehicle parameter optimization

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MODENA: a multi-objective RNA inverse folding

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Advances and applications in bioinformatics and chemistry : AABC 2011年 4卷 1-12页

作者： Akito Taneda Graduate School of Science and Technology Hirosaki University Hirosaki Japan.

Artificially synthesized RNA molecules have recently come under study since such molecules have a potential for creating a variety of novel functional molecules. When designing artificial RNA sequences, secondary structure should be taken into account since functions of noncoding RNAs strongly depend on their structure. RNA inverse folding is a methodology for computationally exploring the RNA sequences folding into a user-given target structure. In the present study, we developed a multi-objective genetic algorithm, MODENA (multi-objective DEsign of Nucleic Acids), for RNA inverse folding. MODENA explores the approximate set of weak Pareto optimal solutions in the objective function space of 2 objective functions, a structure stability score and structure similarity score. MODENA can simultaneously design multiple different RNA sequences at 1 run, whose lowest free energies range from a very stable value to a higher value near those of natural counterparts. MODENA and previous RNA inverse folding programs were benchmarked with 29 target structures taken from the Rfam database, and we found that MODENA can successfully design 23 RNA sequences folding into the target structures; this result is better than those of the other benchmarked RNA inverse folding programs. The multi-objective genetic algorithm gives a useful framework for a functional biomolecular design. Executable files of MODENA can be obtained at http://***/modena/.

关键词： RNA sequence design Rfam multi-objective genetic algorithm secondary structure

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An adaptive genetic algorithm approach for the mixed-model assembly line sequencing problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2010年第17期48卷 5157-5179页

作者： Akgunduz, Onur Serkan Tunali, Semra Dokuz Eylul Univ Dept Ind Engn Izmir Turkey

A mixed-model assembly line (MMAL) is a type of production line that is capable of producing a variety of different product models simultaneously and continuously. The design and planning of such lines involve several long- and short-term problems. Among these problems, determining the sequence of products to be produced has received considerable attention from researchers. This problem is known as the Mixed-Model Assembly Line Sequencing Problem (MMALSP). This paper proposes an adaptive genetic algorithm approach to solve MMALSP where multiple objectives such as variation in part consumption rates, total utility work and setup costs are considered simultaneously. The proposed approach integrates an adaptive parameter control (APC) mechanism into a multi-objective genetic algorithm in order to improve the exploration and exploitation capabilities of the algorithm. The APC mechanism decides the probability of mutation and the elites that will be preserved for succeeding generations, all based on the feedback obtained during the run of the algorithm. Experimental results show that the proposed adaptive GA-based approach outperforms the non-adaptive algorithm in both solution quantity and quality.

关键词： mixed-model assembly line sequencing mixed-model sequencing genetic algorithm multi-objective genetic algorithm

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multi-objective genetic algorithms for Trajectory Optimization of Space Manipulator

Multi-Objective Genetic Algorithms for Trajectory Optimizati...

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4th IEEE Conference on Industrial Electronics and Applications

作者： Liu, Zhengxiong Huang, Panfeng Yan, Jie Liu, Gang Northwestern Polytech Univ Res Ctr Intelligent Robot Coll Astronaut Xian 710072 Peoples R China Infineon Technol Xian Ltd Xian Peoples R China

ISBN: (纸本)9781424427994

This paper propose a multi-objective optimization algorithm to optimize the motion path of space manipulator with multi-objective function. In this formulation, multi-objective genetic algorithm (MOGA) is used to minimize the multi-objective function. The planning procedure is performed in joint space and with respect to all constraints, such as joint angle constraints, joint velocity constraints, torque constraints. We use a MOGA to search the optimal joint inter-knot parameters in order to realize the optimal motion trajectory for space manipulator. These joint inter-knot parameters mainly include joint angle and joint angular velocities. The simulation results test that the proposed multi-objective genetic algorithm has satisfactory performance.

关键词： Space manipulator multi-objective genetic algorithm optimization

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