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检索条件"主题词=Multiobjective Evolutionary Algorithm"

共 172 条记录，以下是141-150 订阅

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Hyperheuristic Codification for the Multi-Objective 2D Guillotine Strip Packing Problem

Hyperheuristic Codification for the Multi-Objective 2D Guill...

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2010 IEEE World Congress on Computational Intelligence

作者： Miranda, Gara de Armas, Jesica Segura, Carlos Leon, Coromoto Univ La Laguna Dpto Estadist IO & Computac San Cristobal la Laguna 38271 Santa Cruz Ten Spain

ISBN: (纸本)9781424481262

Most research on Strip Packing Problems is focused on the single-objective formulation of the problem. However, in this work we deal with a more general and practical variant of the problem, which not only seeks to optimize the usage of the raw material, but also the production process. For the problem solution, we have applied some of the most-known multi-objective evolutionary algorithms, since they have shown a promising behavior when affording multi-objective real-world problems. For an initial implementation, we proposed a solution codification which is based on a complete representation of the pattern layouts. Such an approach was promising but wasn't suitable to afford large instances. For this reason, we have focused on the design of a codification which can be much more competitive when compared to some tailor-made methods. In this sense, we present a hyperheuristic-based codification as an alternative to combine heuristics in such a way that a heuristic's strengths make up for the drawbacks of another. Results demonstrate the advantage of using multi-objective approaches, hyperheuristic-based representations, and of course, the importance on the selection of appropriate solution codifications.

关键词： bin packing codification design cutting evolutionary computation hyperheuristic codification multiobjective 2D guillotine strip packing problem multiobjective evolutionary algorithm multiobjective real-world problem pattern layout production process raw material raw materials

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A Multi-Objective Approach for Community Detection in Complex Network

A Multi-Objective Approach for Community Detection in Comple...

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2010 IEEE World Congress on Computational Intelligence

作者： Shi, Chuan Zhong, Cha Yan, Zhenyu Cai, Yanan Wu, Bin Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Fair Isaac Corp Dept Res San Rafael CA 94903 USA

ISBN: (纸本)9781424481262

Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most contemporary community detection algorithms employ single optimization criteria (e. g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). Correspondingly, a special Multi-Objective evolutionary algorithm (MOEA) is designed to solve the MOP and two model selection methods are proposed. The experiments in artificial and real networks show that the multi-objective community detection algorithm is able to discover more accurate community structures.

关键词： complex network complex networks evolutionary computation multiobjective community structure detection algorithm multiobjective evolutionary algorithm multiobjective optimization problem network theory (graphs) optimization criteria two model selection method

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Planar Monopole Ultra-Wide Band Antenna Optimized by Jumping Genes Genetic algorithm

Planar Monopole Ultra-Wide Band Antenna Optimized by Jumping...

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IEEE International Conference on Industrial Technology

作者： Yang, Xue-Song Man, Kim Fung Yeung, Sai Ho Wang, Bing-Zhong City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Univ Elect Sci & Technol China Inst Appl Phys Chengdu 610054 Peoples R China

ISBN: (纸本)9781424417056

A novel Multi-Objective evolutionary algorithm (MOEA)-Jumping Genes Genetic algorithm (JGGA) is used to design planar Ultra-Wide Band (UWB) antennas. 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 and stable radiated power over the ultra-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 are in reasonable agreement. The group delay which is an indication of linearity between two proposed antennas is also good.

关键词： multiobjective evolutionary algorithm Jumping Genes Genetic algorithm UWB antenna planar monopole

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A new index for evolutionary algorithms to measure the noise level in noisy environments 8

A new index for evolutionary algorithms to measure the noise...

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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Hu, Dongke Li, Junhua Chen, Hao Nanchang Hangkong Univ Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781538682463

In noisy situation, multiobjective evolutionary algorithm will lose the ability to search the optimum results. The reason of this phenomenon is evolutionary algorithm shows the disorder of domination relationship to population individual. Focusing to this problem, lots of researchs have been made. One of them is the method of resampling. The method is indeed improving the bad domination relationship of population individual in the noisy situation. However, there is no clear data parameter to show the degree of improving domination relationship when using the resampling method. So this paper presents a new index as a proof that with the increase of the number of resampling times, the proportion of correct judgment in the noisy situation of multiobjective evolutionary algorithm's domination relationship is also increasing.

关键词： multiobjective evolutionary algorithm domination relationship resampling

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Pareto Partial Dominance MOEA and Hybrid Archiving Strategy Included CDAS in Many-Objective Optimization

Pareto Partial Dominance MOEA and Hybrid Archiving Strategy ...

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2010 IEEE World Congress on Computational Intelligence

作者： Sato, Hiroyuki Aguirre, Hernan E. Tanaka, Kiyoshi Univ Electrocommun Fac Informat & Engn 1-5-1 Choufugaoka Tokyo 1828585 Japan Shinshu Univ Int Young Researcher Empowerment Ctr Wakasato Nagano 3808553 Japan Shinshu Univ Fac Engn Wakasato Nagano 3808553 Japan

ISBN: (纸本)9781424481262

In this work, we propose a novel multi-objective evolutionary algorithm (MOEA) that uses Pareto partial dominance, which calculates dominance between solutions using only r objective functions selected from m objective functions to induce appropriate selection pressure in the evolution process of MOEA. Also, we temporally switch r objective functions among C-m(r) combinations in every interval generations I-g to optimize all of the objective functions throughout the entire evolution process. In this work, we use many-objective 0/1 knapsack problems to verify the search performance of the proposed Pareto partial dominance MOEA (PPD-MOEA). Simulation results show that there is an optimum value for the number of objective functions r to be considered in Pareto partial dominance, and the interval (generation numbers) I-g to maximize the entire search performance. Also, the search performance of PPD-MOEA is superior to NSGA-II and recent state-of-the-art MOEAs, i.e., IBEA, CDAS and MSOPS. Additionally, to further enhance the search performance of PPD-MOEA, we propose a hybrid archiving strategy which uses both conventional NSGA-II and CDAS to select well-spread and well-converged solutions simultaneously when updating the archive population. Simulation results show that the hybrid archiving strategy further improves the search performance of PPD-MOEA by enhancing convergence while maintaining diversity in the archive population.

关键词： MOEA Pareto optimisation Pareto partial dominance evolutionary computation multiobjective evolutionary algorithm objective function optimization search performance

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MOEA Based Hierarchical Fuzzy Control over the Set of User-Defined Initial Conditions

MOEA Based Hierarchical Fuzzy Control over the Set of User-D...

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2010 IEEE World Congress on Computational Intelligence

作者： Zajaczkowski, Juliusz Verma, Brijesh Cent Queensland Univ Rockhampton Qld 4702 Australia

ISBN: (纸本)9781424469208

A compositional method for hierarchical fuzzy control over the user-defined set of initial conditions is presented. The control system is found by using a multiobjective evolutionary algorithm. The inverted pendulum system is selected as an example of a dynamical system and used to test the proposed method. The pre-defined set of initial conditions includes dynamical and static conditions of the system. Control system is designed as a three-layered hierarchical fuzzy logic structure. The test results showed that the proposed method has a relatively high success rate in terms of the number of initial conditions from which the system is controlled to TR. The best achieved result was 94.5% success rate. The proposed compositional method can be applied to a wide range of dynamical systems.

关键词： MOEA dynamical system evolutionary computation fuzzy control hierarchical fuzzy control inverted pendulum system multiobjective evolutionary algorithm nonlinear systems pendulums three-layered hierarchical fuzzy logic structure user-defined initial conditions

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A Fast Method of Constructing the Non-Dominated Set: Arena's Principle

A Fast Method of Constructing the Non-Dominated Set: Arena's...

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4th International Conference on Natural Computation (ICNC 2008)

作者： Tang, Suqin Cai, Zixing Zheng, Jinhua Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Guangxi Normal Univ Sch Comp Sci & Informat Engn Guiyang 541004 Peoples R China Xiangtan Univ Inst Informat Technol Xiangtan 411105 Peoples R China

ISBN: (纸本)9780769533049

The objectives in multiobjective optimization problem (MOP) conflict with each others in usually, and no single solution can optimize all the objectives at the same time. An important task in multiobjective optimization is to identify the set of Pareto-optimal solutions. The Pareto front is the set of all the optimal tradeoffs in the objective space. Constructing a non-dominated set is an important process in the multiobjective evolutionary algorithm (MOEAs). In this paper we introduce a fast method for constructing the non-dominated set, called the arena's principle (AP). The method with O(m (n) over barm) computational complexity is presented Experimental results have demonstrated that the computational complexity of the arena's principle is better than the fast non-dominated sorting approach in NSGA-II

关键词： multiobjective evolutionary algorithm Pareto-Optimal front non-dominated set

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Optimal Spatial Sampling of Hyperspectral Imagery for Fusion with Panchromatic Video in Multitarget Tracking

Optimal Spatial Sampling of Hyperspectral Imagery for Fusion...

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4th IEEE Sensors Applications Symposium

作者： Secrest, Barry R. Vasquez, Juan R. USAF Inst Technol Dayton OH 45429 USA Numer Corp Beavercreek OH 45431 USA

ISBN: (纸本)9781424427864

Hyperspectral imagery (HSI) data has proven useful for discriminating targets, however the relatively slow speed at which HSI data is gathered for an entire frame reduces the usefulness of fusing this information with panchromatic video. Additionally, the volume of HSI information collected affects the computational performance of software exploiting the information. Paradoxically, it is too much information and we cannot get enough of it. A new sensor under development has the potential of overcoming this problem. It has the ability to provide HSI data for a limited number of pixels while providing panchromatic video for the remainder of the pixels. The HSI data is co-registered with the panchromatic video and is available at each frame. This paper investigates the exploitation of this new sensor for target tracking. The first challenge of exploiting this sensor is to determine where the gathering of HSI data will be the most useful as compared to collecting panchromatic. We optimize the selection of pixels for which we will gather HSI data. We refer to this as spatial sampling. Spatial sampling is solved using a utility function where pixels receive a value based on their nearness to a target of interest (TOI). The TOIs are determined from the tracking algorithm providing a close coupling of the tracking and the sensor control. The weighting of the different types of TOIs is accomplished by a multiobjective genetic algorithm. Experiments compare fused versus non-fused tracking performance.

关键词： Multitarget Tracking Hyperspectral Imagery Data Fusion multiobjective evolutionary algorithm

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A Layered Approach for Enabling Demand Side Management in Smart Grid 5

A Layered Approach for Enabling Demand Side Management in Sm...

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5th International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Li, Dan Sun, Hongjian Chiu, Wei-Yu Univ Durham Sch Engn & Comp Sci Durham DH1 3LE England Yuan Ze Univ Dept Elect Engn Taoyuan 32003 Taiwan

ISBN: (纸本)9781509006502

Smart grid (SG) represents intelligent technologies used to address the climate change. Demand side management (DSM) is an essential part of the SG. This paper establishes a layered model for the DSM. The model involves three participants: power generators, including renewable energy sources, demand response (DR) aggregator, and consumers. The revenue of the DR aggregator is analyzed. The discomfortable level caused by the DSM is considered for consumers. This model leads to a multiobjective (MO) problem. An MO evolutionary algorithm is used to find the Pareto front, facilitating the selection of a fair solution. Simulation results illustrate the feasibility of the proposed approach.

关键词： Demand side management demand response aggregator multiobjective problem multiobjective evolutionary algorithm smart grid

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Circuit-Simulation-Based Multi-objective evolutionary algorithm with Multi-Level Clock Driving Technique for a-Si:H TFTs Gate Driver Circuit Design Optimization

Circuit-Simulation-Based Multi-objective Evolutionary Algori...

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Workshop on Computer Architecture, Embedded Systems, SoC, and VLSI/EDA / International Computer Symposium (ICS)

作者： Hung, Sheng-Chin Chen, Chieh-Yang Chiang, Chien-Hsueh Li, Yiming Natl Chiao Tung Univ Parallel & Sci Comp Lab 1001 Ta Hsueh Rd Hsinchu 300 Taiwan Natl Chiao Tung Univ Inst Biomed Engn Hsinchu 300 Taiwan Natl Chiao Tung Univ Inst Commun Engn Hsinchu 300 Taiwan Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 300 Taiwan

ISBN: (纸本)9781614994848;9781614994831

In this work, dynamic characteristic of a new amorphous silicon gate (ASG) driver circuit is optimized by using multi-objective evolutionary algorithm (MOEA) and hydrogenated amorphous silicon (a-Si:H) TFT circuit simulator on the unified optimization framework (UOF). The ASG driver circuit consisting of 14 a-Si:H TFTs is optimized for the given specifications of the fall time < 3 mu s and the ripple voltage < -9 V with simultaneously minimizing the total layout area. More than 50% reductions on the falling time of the ASG driver circuit have been achieved by using the proposed optimization methodology together with a novel 3-level clock driving technique.

关键词： a-Si:H TFTs amorphous silicon gate driver circuits display panel design optimization multiobjective evolutionary algorithm unified optimization framework

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