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7th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2010)

作者： Mondal, Kartick Chandra Mukhopadhyay, Anirban Maulik, Ujjwal Bandhyapadhyay, Sanghamitra Pasquier, Nicolas Univ Nice Sophia Antipolis Lab I3S CNRS UMR 6070 F-06108 Nice France Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 W Bengal India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

ISBN: (纸本)9783642219450

Microarray experiments generate a large amount of data which is used to discover the genetic background of diseases and to know the characteristics of genes. Clustering the tissue samples according to their co-expressed behavior and characteristics is an important tool for partitioning the dataset. Finding the clusters of a given dataset is a difficult task. This task of clustering is even more difficult when we try to find the rank of each gene, which is known as Gene Ranking, according to their abilities to distinguish different classes of samples. In the literature, many algorithms are available for sample clustering and gene ranking or selection, separately. A few algorithms are also available for simultaneous clustering and feature selection. In this article, we have proposed a new approach for clustering the samples and ranking the genes, simultaneously. A novel encoding technique for the chromosomes is proposed for this purpose and the work is accompleshed using a multi-objective evolutionary technique. Results have been demonstrated for both artificial and real-life gene expression data sets.

关键词： multi-objective evolutionary algorithm Gene Ranking Clustering Gene Expression Data

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A Cooperative multi-objective Island Parallel Model for Wind Farm Planning

A Cooperative Multi-Objective Island Parallel Model for Wind...

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2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering

作者： Marquez, A. L. Gil, C. Banos, R. Montoya, M. G. Montoya, F. G. Manzano-Agugliaro, F. Univ Almeria Dept Comp Architecture & Elect La Canada De San Urbano Almeria Spain Univ Almeria Dept Rural Engn La Canada De San Urbano Almeria Spain

ISBN: (纸本)9781905088423

Wind energy is widely used to produce electricity all over the world, but it is not a reliable and constant source. To solve this issue, the use of statistics over an extended period of time is advisable, in order to quantify an average amount of energy production for the wind farm. This means that it is possible to identify three distinct objectives in conflict for the problem at hands: maximizing the energy production, reducing the amount of unused installed power, and minimizing the standard deviation of the energy produced through the day. To that end, the data saved through year 2008 in Cancun, Mexico, are used in order to generate the wind regime through an average day. The power curve of 26 different aerogenerator models is also used. To solve this three-objective problem, it is advisable to use a multi-objective evolutionary algorithm (MOEA), but most of them work better for 2-objective problems. This is solved by using a Hybrid Island Parallel MOEA, that combines 4 different MOEAs (NSGA-II, SPEA2, PESA, msPESA) and also different sets of objectives for different islands. This means that an adaption operation is needed in order to migrate solutions between islands, and also in order to grow from two objectives on each island to the final three-objective solutions returned by the procedure.

关键词： hybrid algorithm threaded computation island parallel model multi-objective evolutionary algorithm wind farm planning aerogenerators

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multi-objective Particle Swarm Optimization with Preference-based Sorting

Multi-Objective Particle Swarm Optimization with Preference-...

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IEEE Congress on evolutionary Computation (CEC)

作者： Lee, Ki-Baek Kim, Jong-Hwan Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea

ISBN: (纸本)9781424478354

multi-objective particle swarm optimization (MOPSO) provides a set of nondominated solutions and the number of nondominated solutions increases exponentially when the number of objectives increases. To select a desired solution out of them, preference-based solution selection algorithm (PSSA) was proposed by incorporating user's preference into multi-objective evolutionary algorithms. In this paper, multi-objective particle swarm optimization with preference-based sorting (MOPSO-PS) is proposed, where a global best position is randomly selected from the archive of nondominated solutions sorted by global evaluation considering user's preferences for multiple objectives. The user's preference is represented as a degree of consideration for the objectives by the fuzzy measures. The global evaluation of the solutions is carried out by the fuzzy integral of partial evaluation with respect to the fuzzy measures, where the partial evaluation of each solution is obtained as a normalized objective function value. To demonstrate the effectiveness of the proposed MOPSO-PS, empirical comparisons to NSGA-II, MQEA, and MOPSO are carried out for the DTLZ functions. Experimental results show that the user's preference is properly reflected in the selected solutions without any loss of overall quality and diversity.

关键词： Swarm Intelligence Particle Swarm Optimization multi-objective evolutionary algorithm Fuzzy Integral Fuzzy Measure Preference-based Solution Selection algorithm

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Sizing mixed-mode circuits by multi-objective evolutionary algorithms

Sizing mixed-mode circuits by multi-objective evolutionary a...

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53rd Midwest Symposium on Circuits and Systems (MWSCAS 2010)

作者： Guerra-Gomez, I. Tlelo-Cuautle, E. McConaghy, Trent de la Fraga, Luis G. Gielen, Georges Reyes-Salgado, G. Munoz-Pacheco, J. M. INAOE Department of Electronics. M&#x00E9 xico

ISBN: (纸本)9781424477739

We show the behavior of the generations of two multi-objective evolutionary algorithms (MOEAs) for the optimal sizing of two mixed-mode circuits. The non-sorting genetic algorithm (NSGA-II), and the MOEA based on decomposition (MOEA/D) are used to size a second generation current conveyor (CCII+) and a current-feedback operational amplifier (CFOA). Both MOEAs take into account design constraints, and link HSPICE to evaluate the electrical characteristics of the CCII+ and CFOA. Differential evolution is used as genetic operator to show the behavior of the generations of the two MOEAs.

关键词： current conveyors SIZING evolutionary algorithm Hybrid modes design constraints multi-objective evolutionary algorithm Electric circuits ELECTRICAL CHARACTERISTICS current feedback operational amplifier Genetic Operator

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Ultra-Wideband Planar Antenna Optimized by a multi-objective evolutionary algorithm

Ultra-Wideband Planar Antenna Optimized by a Multi-Objective...

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2007 Asia Pacific Microwave Conference (APMC 2007), vol.3

作者： Xue-Song Yang Kim Fung Man Sai Ho Yeung Jia-Lin Li Bing-Zhong Wang 2 Institute of Applied Physics University of Electronic Science and Technology of China Chengdu China 1 Department of Electronic Engineering City University of Hong Kong Kowloon Hong Kong SAR China

An ultra-wideband (UWB) planar antenna optimized by a newly developed multi-objective evolutionary algorithm-^sJumping Genes Genetic algorithm (JGGA) is proposed. The optimization procedure aims at finding an antenna not only with low voltage standing wave ratio (VSWR) and small size, but also with stable radiation pattern over the wide band. There are five objective functions and they are based on VSWR, antenna size, radiation pattern and gain. Both the simulated and measured results are given and they agree well with each other.

关键词： multi-objective evolutionary algorithm Jumping Genes Genetic algorithm UWB antenna

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Molecular Library Design Using multi-objective Optimization Methods

Molecular Library Design Using Multi-Objective Optimization ...

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作者： Christos A. Nicolaou Christos C. Kannas

Advancements in combinatorial chemistry and high-throughput screening technology have enabled the synthesis and screening of large molecular libraries for the purposes of drug discovery. Contrary to initial expectations, the increase in screening library size, typically combined with an emphasis on compound structural diversity, did not result in a comparable increase in the number of promising hits found. In an effort to improve the likelihood of discovering hits with greater optimization potential, more recent approaches attempt to incorporate additional knowledge to the library design process to effectively guide the search. multi-objective optimization methods capable of taking into account several chemical and biological criteria have been used to design collections of compounds satisfying simultaneously multiple pharmaceutically relevant objectives. In this chapter, we present our efforts to implement a multi-objective optimization method, MEGALib, custom-designed to the library design problem. The method exploits existing knowledge, e.g. from previous biological screening experiments, to identify and profile molecular fragments used subsequently to design compounds compromising the various objectives. less

关键词： High-throughput Screening Molecular Modeling multi-objective molecular library design multi-objective evolutionary algorithm selective library design MEGALib

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The Pareto Optimization of Wide-Band Conformal Antenna Arrays

The Pareto Optimization of Wide-Band Conformal Antenna Array...

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2011 IEEE International Symposium on Antennas and Propagation(APSURSI 2011)

作者： Davide Bianchi Agostino Monorchio Simone Genovesi Alessandro Corucci Douglas H. Werner Ping L. Werner Dip. Ingegneria dell'Informazione University of Pisa Pisa Italy Dept. of Electrical Engineering The Pennsylvania State University University Park PA USA 16802

ISBN: (纸本)9781424495627;9781424495634

A design procedure for synthesizing wideband conformal antenna arrays based on a multi-objective evolutionary algorithm is presented. In this paper, raised power series (RPS) are employed as a simple yet effective way to introduce aperiodicity into a conformal semi-circular phased antenna array for achieving wideband performance. Unlike conventional linear array synthesis methods where, for example, the genetic algorithm (GA) has been utilized to meet a single design objective, the multi-objective optimization technique proposed in this paper employs the nondominated sorting GA version II (NSGA-II).

关键词： multi-objective optimization genetic algorithm (GA) Pareto wideband arrays conformal array raised power series Pareto optimisation conformal antennas design process multi-objective evolutionary algorithm design target Antenna arrays broadband antenna phased arrays Gallium

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multi-objective evolutionary emergency response optimization for major accidents

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JOURNAL OF HAZARDOUS MATERIALS 2010年第1-3期178卷 792-803页

作者： Georgiadou, Paraskevi S. Papazoglou, Ioannis A. Kiranoudis, Chris T. Markatos, Nikolaos C. Natl Tech Univ Athens Sch Chem Engn Athens 15780 Greece Natl Ctr Sci Res Demokritos Syst Reliabil & Ind Safety Lab Athens 15310 Greece

Emergency response planning in case of a major accident (hazardous material event, nuclear accident) is very important for the protection of the public and workers' safety and health. In this context, several protective actions can be performed, such as, evacuation of an area: protection of the population in buildings: and use of personal protective equipment. The best solution is not unique when multiple criteria are taken into consideration (e.g. health consequences, social disruption, economic cost). This paper presents a methodology for multi-objective optimization of emergency response planning in case of a major accident. The emergency policy with regards to protective actions to be implemented is optimized. An evolutionary algorithm has been used as the optimization tool. Case studies demonstrating the methodology and its application in emergency response decision-making in case of accidents related to hazardous materials installations are presented. However, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency response procedures in other cases (nuclear accidents, transportation of hazardous materials) or for land-use planning issues. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Emergency planning Major accident Evacuation multi-objective evolutionary algorithm Shelter-in-place

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multi-objective Optimal Tuning of multi-loop Control Systems by using evolutionary Computation

Multi-objective Optimal Tuning of Multi-loop Control Systems...

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Asian Control Conference

作者： Andrea Richter Adrian Gambier Westinghouse Electric Germany Heidelberg University

ISBN: (纸本)9781612844879

A framework for the joint parameters tuning of several controllers in coupled multi-loop control systems is presented. It is based on the design of a cooperative dynamic game, which is solved by using a multi-objective evolutionary algorithm, combined with a game theoretic solution concept. For each controller, an objective function is defined and parametrically optimized. Several performance indices e.g. ISE, ITSE and ISTSE are used as objective functions and the optimization results are compared by means of a simulation example.

关键词： evolutionary algorithm multiple loop objective function performance indicators control systems solution concept multi-objective evolutionary algorithm Controllers tuning control ring Hunting Game theory Game animals

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Time-Jerk Synthetic Optimal Trajectory Planning of Robot Using Strength Pareto evolutionary algorithm

Time-Jerk Synthetic Optimal Trajectory Planning of Robot Usi...

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International Conference on Advanced Measurement and Test (AMT 2010)

作者： Yan, Jie Gong, Daoxiong Beijing Univ Technol Sch Elect Informat & Control Engn Beijing 100124 Peoples R China

ISBN: (纸本)9780878492695

The Strength Pareto evolutionary algorithm (SPEA) is adopted to find time-jerk synthetic optimal trajectory of a hexapod robot in the joint space. In order to get the optimal trajectory, cubic splines are employed and derived under the constraint condition of via points to assure overall continuity of velocity and acceleration. Taken both the execution time and minimax approach of jerk as objectives, and expressed the kinematics constraints as upper bounds on the absolute values of velocity and acceleration, the mathematic model of time-jerk synthetic optimal trajectory planning is built. Finally, SPEA is adopted to optimize the stair-climbing trajectory of a hexapod robot, the simulation results show that this method can solve the trajectory planning problem effectively, and the stair-climbing trajectory can meet the contradictory objective functions of high speed and low robot vibration well.

关键词： multi-objective evolutionary algorithm minimum time-jerk cubic splines hexapod robot stair-climbing

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