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2010 IEEE World Congress on Computational Intelligence

作者： Kaufmann, Paul Knieper, Tobias Platzner, Marco Univ Gesamthsch Paderborn Dept Comp Sci D-33098 Paderborn Germany

ISBN: (纸本)9781424481262

This work investigates the effects of the periodization of local and global multi-objective search algorithms. To this, we introduce a model for periodization and define a new multi-objective evolutionary algorithm adopting concepts from evolutionary Strategies and NSGAII. We show that our method, especially when periodized with standard multi-objective genetic algorithms, excels for the evolution of digital circuits on the Cartesian Genetic Programming model as well as on some standard benchmarks such as the ZDT6.

关键词： Cartesian genetic programming genetic algorithms hybrid evolutionary strategy multi-objective evolutionary algorithm multi-objective genetic optimizers multi-objective search algorithms periodization search problems

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A Novel Approach to multi-level evolutionary Design Optimization of a MEMS Device

A Novel Approach to Multi-level Evolutionary Design Optimiza...

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9th International Conference on Electron Spectroscopy

作者： Farnsworth, Michael Benkhelifa, Elhadj Tiwari, Ashutosh Zhu, Meiling Cranfield Univ Decis Engn Ctr Coll Rd Cranfield MK43 0AL Beds England Cranfield Univ Microsyst Nanotechnol Ctr Cranfield MK43 0AL Beds England

ISBN: (纸本)9783642153228

This paper introduces a novel approach to the evolutionary design optimisation of an MEMS bandpass filter, incorporating areas of multi-disciplinary, multi-level and multi-objective design optimisation in the process. In order to demonstrate this approach a comparison is made to previous attempts to design similar bandpass filters, providing comparable results at a significant reduction in functional evaluations. In this endeavour, a circuit equivalent of the MEMS bandpass filter is evolved extrinsically using the SPICE Simulator.

关键词： multi-Disciplinary Optimisation multi-objective evolutionary algorithm multi-Level Optimisation MEMS Micro-Electro-Mechanical Systems Extrinsic Evolution

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multi-objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems

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IEEE TRANSACTIONS ON RELIABILITY 2010年第3期59卷 563-575页

作者： Wang, Zai Tang, Ke Yao, Xin Univ Sci & Technol China Sch Comp Sci & Technol NICAL Hefei 230027 Anhui Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

Software testing is an important issue in software engineering. As software systems become increasingly large and complex, the problem of how to optimally allocate the limited testing resource during the testing phase has become more important, and difficult. Traditional Optimal Testing Resource Allocation Problems (OTRAPs) involve seeking an optimal allocation of a limited amount of testing resource to a number of activities with respect to some objectives (e.g., reliability, or cost). We suggest solving OTRAPs with multi-objective evolutionary algorithms (MOEAs). Specifically, we formulate OTRAPs as two types of multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. Second, the total testing resource consumed is also taken into account as the third objective. The advantages of MOEAs over state-of-the-art single objective approaches to OTRAPs will be shown through empirical studies. Our study has revealed that a well-known MOEA, namely Nondominated Sorting Genetic algorithm II (NSGA-II), performs well on the first problem formulation, but fails on the second one. Hence, a Harmonic Distance Based multi-objective evolutionary algorithm (HaD-MOEA) is proposed and evaluated in this paper. Comprehensive experimental studies on both parallel-series, and star-structure modular software systems have shown the superiority of HaD-MOEA over NSGA-II for OTRAPs.

关键词： multi-objective evolutionary algorithm parallel-series modular software system software engineering software reliability software testing star-structure modular software system

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An adaptive time discretization of the classical and the dual porosity model of Richards' equation

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2010年第12期233卷 3167-3177页

作者： Kuraz, Michal Mayer, Petr Leps, Matej Trpkosova, Dagmar Czech Univ Life Sci Prague Fac Environm Sci Dept Water Resources & Environm Modeling Prague 16521 6 Czech Republic Czech Tech Univ Fac Civil Engn Dept Math CR-16635 Prague Czech Republic Czech Tech Univ Fac Civil Engn Dept Mech CR-16635 Prague Czech Republic Charles Univ Prague Fac Sci Inst Hydrogeol CR-11636 Prague 1 Czech Republic

This paper presents a numerical solution to the equations describing Darcian flow in a variably saturated porous medium-a classical Richards' equation model Richards (193 1) [1] and an extension of it that approximates the flow in media with preferential paths-a dual porosity model Gerke and van Genuchten (1993) [8]. A numerical solver to this problem, the DRUtES computer program, was developed and released during our investigation. A new technique which maintains an adaptive time step, defined here as the Retention Curve Zone Approach, was constructed and tested. The aim was to limit the error of a linear approximation to the time derivative part. Finally, parameter identification was performed in order to compare the behavior of the dual porosity model with data obtained from a non-homogenized fracture and matrix flow simulation experiment. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Darcy's law Variable saturation Retention curve Mass balance Adaptive time discretization Preferential flow Homogenization Parameter identification multi-objective evolutionary algorithm

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MOEA based design of decentralized controllers for LFC of interconnected power systems with nonlinearities, AC-DC parallel tie-lines and SMES units

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ENERGY CONVERSION AND MANAGEMENT 2010年第5期51卷 873-880页

作者： Ganapathy, S. Velusami, S. Annamalai Univ Dept Elect Engn Annamalainagar 608002 Tamil Nadu India

A new design of multi-objective evolutionary algorithm based decentralized controllers for load-frequency control of interconnected power systems with Governor Dead Band and Generation Rate Con straint nonlinearities, AC-DC parallel tie-lines and Superconducting Magnetic Energy Storage (SMES) units, is proposed in this paper. The HVDC link is used as system interconnection in parallel with AC tie-line to effectively damp the frequency oscillations of AC system while the SMES unit provides bulk energy storage and release, thereby achieving combined benefits. The proposed controller satisfies two main objectives, namely, minimum Integral Squared Error of the system output and maximum closed-loop stability of the system. Simulation studies are conducted on a two area interconnected power system with nonlinearities, AC-DC tie-lines and SMES units. Results indicate that the proposed controller improves the transient responses and guarantees the closed-loop stability of the overall system even in the presence of system nonlinearities and with parameter changes. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Load-frequency control multi-objective evolutionary algorithm AC-DC tie-lines Governor Dead Band Generation Rate Constraint Superconducting Magnetic Energy Storage

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Efficient resource allocation for genetic algorithm based multi-objective optimization with 1,000 simulations

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STRUCTURAL AND multiDISCIPLINARY OPTIMIZATION 2010年第3期41卷 421-432页

作者： Goel, Tushar Stander, Nielen Lin, Yih-Yih Livermore Software Technol Corp Livermore CA USA Hewlett Packard Corp High Performance Comp Div Marlborough MA USA

This study pertains to practical use of the GA for industrial applications where only a limited number of simulations can be afforded. Specifically, an attempt is made to find an efficient allocation of the total simulation budget (population size and number of generations) for constrained multi-objective optimization. A study is conducted to seek improvements while restricting the number of simulations to 1,000. Parallelization is exploited using concurrent simulations for each GA generation on a HP quad-core cluster, and resulted in a significant time savings. Furthermore, the efficient distribution of computational effort to achieve the greatest improvement in performance was explored. Two analytical examples as well as an automotive crashworthiness simulation of a finite element model with 58,000 elements were used as test examples. Various population sizes and numbers of generations were tried while limiting the total number of simulations to 1,000. The optimization performance was compared with Monte-Carlo and space filling sampling methods. It was observed that using the GA, many feasible and trade-off solutions could be found. It is shown that allowing a large number of generations is beneficial to get good trade-off solutions. For the vehicle design, significant improvements in the performance were observed. This example also suggests that, for problems with a small feasible region, the number of feasible solutions can be significantly increased in the first few generations involving about 200 simulations.

关键词： multi-objective evolutionary algorithm Crashworthiness Population sensitivity Resource allocation NSGA-II Optimization

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A heuristic non-dominated sorting genetic algorithm-II for satellite-module layout optimisation

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2010年第1-3期38卷 228-235页

作者： Xu, Y. Li, C. Shi, Y. J. Teng, H. F. Dalian Univ Technol Dept Comp Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol Dept Mech Engn Dalian 116024 Peoples R China

This paper proposes a Heuristic Non-dominated Sorting Genetic algorithm-II (HNSGA-II) to solve a layout optimisation problem such as that of satellite-modules. Firstly, we apply a diversity degree method to evaluate the dominance (or inferiority) of solutions in the same front instead of the crowding-distance method. Secondly, a filter technique of a Pareto solution set is introduced to preserve the elite individuals more effectively. Thirdly, three kinds of operators are adopted to improve search performance. Finally, we give two heuristic layout strategies in the optimisation process. The numerical experiments of a simplified satellite-module layout design show the feasibility and the effectiveness of the proposed algorithm.

关键词： layout optimisation satellite-module multi-objective evolutionary algorithm NSGA-II Pareto solution heuristic non-dominated sorting

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A Novel Diversity Preservation Strategy based on Ranking Integration for Solving Some Specific multi-objective Problems

A Novel Diversity Preservation Strategy based on Ranking Int...

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9th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES 2010)

作者： Yu Long Wang Pan Zhu Haoshen Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

ISBN: (纸本)9780769541105

In recent decades, multi-objective evolutionary algorithms (MOEAs) are developed as powerful tools to solve multi-objective optimization problems. While the diversity of Pareto front (PF) plays an important role in the performance evaluation of MOEAs, various diversity preservation strategies (DPS) have been developed. In this paper, a novel approach that inspired from the crowding distance technique is proposed to maintain the diversity of solutions in multi-objective problems (MOPs) with quite different spans of value range. In order to improve its performance, this approach is applied in a well-know MOEA NSGA II by replacing its original DPS. According to 3 test MOPs, the modified NSGA II shows a better diversity and distribution in the PF compared with the original version. Furthermore, the influence of the spans of value range on the performance of original DPS in NSGA II is discussed and the robustness of the new DPS is illustrated.

关键词： multi-objective evolutionary algorithm crowding distance diversity preservation NSGA II

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Web Usage Mining for Improving Students Performance in Learning Management Systems

Web Usage Mining for Improving Students Performance in Learn...

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23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

作者： Zafra, Amelia Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

ISBN: (纸本)9783642130328

An innovative technique based on multi-objective grammar guided genetic programming (MOG3P-MI) is proposed to detect the most relevant activities that a student needs to pass a course based on features extracted from logged data in an education web-based system. A more flexible representation of the available information based on multiple instance learning is used to prevent;the appearance of a great number of missing values. Experimental results with the most relevant proposals in multiple instance learning in recent years demonstrate that MOG3P-MI successfully improves accuracy by finding a balance between specificity and sensitivity values. Moreover, simple and clear classification rules winch are markedly useful to identify the number, type and time of activities that a student should do within the web system to pass a course are provided by our proposal.

关键词： Web Usage Mining Educaticutal Data Mining multiple Instance Learning multi-objective evolutionary algorithm Grammar Guided Genetic Programming

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Hybrid escalating multi-objective evolutionary algorithm with its application to flow shop problems

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Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice 2006年第8期26卷 101-108页

作者： Shi, Rui-Feng Zhou, Hong Shangguan, Chun-Xia School of Economics and Management Beijing University of Aeronautics and Astronautics Beijing 100083 China

A hybrid escalating multi-objective evolutionary algorithm (HEMEA), which has a new evolution structure compared with the existing ones, was proposed in this paper. The new algorithm enhanced the efficiency of optimization by using an innovative escalating evolutionary scheme with an elitism selection and variable Pareto local search strategy. A series of bi-objective flow shop optimization problems from OR-Library and one typical tri-objective flow shop optimization problem which was first studied in Bagchi's work, were re-optimized by NSGA-II, MOGLS, ENGA and our HEMEA respectively. The comparison of the optimization results has shown the outstanding performance of HEMEA with respect to the others', which were well-known for their good performance in multi-objective evolutionary computation. thus, the effectiveness and efficiency of HEMEA were demonstrated.

关键词： Escalating evolutionary Flow shop Local search multi-objective evolutionary algorithm

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