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检索条件"主题词=Multi-objective Evolutionary algorithms"

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Constraint Handling within MOEA/D Through an Additional Scalarizing Function

Constraint Handling within MOEA/D Through an Additional Scal...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Zapotecas-Martinez, Saul Ponsich, Antonin UAM Cuajimalpa Mexico City DF Mexico UAM Azcapotzalco Mexico City DF Mexico

ISBN: (纸本)9781450371285

The multi-objective evolutionary Algorithm based on Decomposition (MOEA/D) has shown high-performance levels when solving complicated multi-objective optimization problems. However, its adaptation for dealing with constrained multi-objective optimization problems (cMOPs) keeps being under the scope of recent investigations. This paper introduces a novel selection mechanism inspired by the epsilon-constraint method, which builds a bi-objective problem considering the scalarizing function (used into the decomposition approach of MOEA/D) and the constraint violation degree as an objective function. During the selection step of MOEA/D, the scalarizing function is considered to choose the best solutions to the cMOP. Preliminary results obtained over a set of complicated test problems drawn from the CF test suite indicate that the proposed algorithm is highly competitive regarding state-of-the-art MOEAs adopted in our comparative study.

关键词： Constrained handling techniques multi-objective evolutionary algorithms decomposition approach scalarizing functions

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Coevolving Collection Plans for UAS Constellations 11

Coevolving Collection Plans for UAS Constellations

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13th Annual Genetic and evolutionary Computation Conference (GECCO)

作者： Stouch, Daniel W. Zeidman, Ernest Richards, Marc McGraw, Kirk D. Callahan, William Charles River Analyt 625 Mt Auburn St Cambridge MA 02138 USA US Army ERDC CERL Cambridge MA 02138 USA

ISBN: (纸本)9781450305570

Our SPARTEN (Spatially Produced Airspace Routes from Tactical Evolved Networks) tool generates coordinated mission plans for constellations of unmanned aerial vehicles by allowing the mission planner to specify the importance of each objective for each mission. Using an evolutionary algorithm-based, multi-objective optimization technique, we consider factors such as area of analysis coverage, restricted operating zones, maximum ground control station range, adverse weather effects, military terrain value, airspace collision avoidance, path linearity, named area of analysis emphasis, and sensor performance. By employing novel visualizations using geographic information systems to represent their effectiveness, we help the user "look under the hood" of the algorithms and understand the viability and effectiveness of the mission plans to identify coverage gaps and other inefficiencies. In this paper, we apply multi-objective evolutionary algorithms to the air mission planning domain, with a focus on the visualization components.

关键词： evolutionary algorithms multi-objective evolutionary algorithms air operations mission planning unmanned systems

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multiobjective Discrete Differential Evolution for Service Restoration in Energy Distribution Systems

Multiobjective Discrete Differential Evolution for Service R...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Sanches, Danilo S. London, Joao Bosco A., Jr. Delbem, Alexandre C. B. Univ Tecnol Fed Parana Cornelio Procopio PR Brazil Univ Sao Paulo Sao Carlos Sch Engn Sao Carlos SP Brazil Univ Sao Paulo Inst Math & Comp Sci Sao Carlos SP Brazil

ISBN: (纸本)9781450343237

This paper presents a new multiobjective discrete differential evolution for service restoration in distribution systems. The proposed approach was compared with other five multiobjective evolutionary algorithms (MOEAs), which use Node-Depth Encoding (NDE). The proposed approach have been evaluated taking into account the switching operations necessary to find adequate restoration plans considering multiple non-linear constraints and objective functions. The MOEAs used in this paper have been employed to solve four different datasets with 3,860, 7,720, 15,440 and 30,880 buses, respectively. Simulations results have shown that proposed approach reached good solutions with low switching operations and reduced running time when compared with others MOEAs.

关键词： Large-Scale Distribution Systems Service Restoration Node-depth Encoding multi-objective evolutionary algorithms Differential Evolution

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A NOVEL DYNAMIC CROWDING DISTANCE BASED DIVERSITY MAINTENANCE STRATEGY FOR MOEAS 16

A NOVEL DYNAMIC CROWDING DISTANCE BASED DIVERSITY MAINTENANC...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yang, Ling Guan, Yuyang Sheng, Weiguo Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China Hangzhou Normal Univ Dept Comp Sci Hangzhou 310036 Zhejiang Peoples R China

ISBN: (纸本)9781538604083

Preserving population diversity is crucial for the performance of multi-objective evolutionary algorithms (MOEAs). In this paper, we propose a novel dynamic crowding distance based diversity preserving strategy for MOEAs. In the proposed strategy, the crowding distance is calculated based on the degree of deviation of each individual to its adjacent neighbors, thus appropriately adjusting the individual's density according to its position. Further, a multi-individual deletion mechanism is introduced to improve the efficiency of the strategy. For evaluation purpose, we incorporate the proposed strategy into NSGA-II and test it on ten functions. Our results show that the proposed strategy is able to improve the performance of NSGA-II and the resulting algorithm outperforms related methods to be compared.

关键词： multi-objective evolutionary algorithms Diversity preserve Dynamic crowding distance

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NSGA-II: Implementation and Performance Metrics Extraction for CPU and GPU 16

NSGA-II: Implementation and Performance Metrics Extraction f...

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16th International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Padurariu, Florina Roxana Marinescu, Cristina Politehn Univ Timisoara Timisoara Romania West Univ Timisoara Timisoara Romania

ISBN: (纸本)9781479984480

multi-objective Optimization evolutionary algorithms are widely employed for solving different real-world optimization problems. Usually their runs involve a considerable amount of time because of the need to evaluate many functions. This particularity makes them good candidates of parallelization. In this work we investigate the benefits of the GPU implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) versus its CPU implementation in terms of the execution time.

关键词： multi-objective evolutionary algorithms GPU CPU empirical software engineering

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A multi-objective Genetic Local Search Algorithm for Optimal Feature Subset Selection

A Multi-objective Genetic Local Search Algorithm for Optimal...

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International Conference on Computational Science and Computational Intelligence (CSIC)

作者： Tian, David Leeds Beckett Univ Leeds Sustainabil Inst Leeds LS1 3HE W Yorkshire England

ISBN: (纸本)9781509055104

Feature selection algorithms select the most relevant features of a data set to improve the classification performance of the machine learning classifiers trained using the data set. This paper proposes a feature selection algorithm called multi-objective genetic local search (MOGLS) which integrates a 3-objective genetic algorithm with a local search heuristic to find feature subsets with the maximum prediction accuracy, the smallest sizes and the minimum redundancy. The performance of MOGLS is compared with 4 algorithms: a wrapper genetic algorithm, correlation-based feature selection, mutual information ranking and C4.5 on 8 datasets from the UCI machine learning repository. MOGLS performs better than or as good as the 4 algorithms on the 8 datasets.

关键词： Machine learning technologies multi-objective evolutionary algorithms Genetic algorithms Metaheuristic optimization algorithms Fuzzy and rough data analysis

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multimodal multi-objective Optimization Using A Density-based One-by-One Update Strategy

Multimodal Multi-objective Optimization Using A Density-base...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Shi, Ruizhi Lin, Wu Lin, Qiuzhen Zhu, Zexuan Chen, Jianyong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781728121536

For real-world optimization problems, a uniformly and widely distributed Pareto optimal set (PS) in the decision space can provide more choices for decision makers. However, most of multi-objective evolutionary algorithms (MOEAs) only consider convergence and diversity in the objective space, which rarely pay attention to diversity in the decision space. Especially for multimodal multi-objective optimization problems (MMOPs), there may exist multiple distinct PSs corresponding to the same Pareto front (PF). Thus, we propose a novel multimodal multi-objective evolutionary algorithm using a density-based one-by-one update strategy in this paper, which considers diversity in both the objective and decision spaces. In the proposed algorithm, once an offspring is generated during evolution, the most crowded subregion with the largest niche count in the objective space has to be identified again, helpful to maintain diversity in the objective space. Furthermore, the harmonic average distance approach is used to estimate the global density of solutions in the decision space, trying to maintain the population's diversity in the decision space. Our proposed algorithm is compared with several state-of-the-art algorithms on MMOPs. The experimental results demonstrate that our algorithm is capable of preserving promising solutions with even distribution in both of decision space and objective space and also shows the superiority on solving the adopted MMOPs.

关键词： multi-objective evolutionary algorithms multimodal multi-objective optimization global density harmonic average distance

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multi-objective Design Optimization of Three-Phase Induction Motor Using NSGA-II Algorithm 1

Multi-objective Design Optimization of Three-Phase Induction...

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1st International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Ranjan, Soumya Mishra, Sudhansu Kumar NIST Dept Elect & Elect Engn Berhampur Orissa India Birla Inst Technol Dept Elect & Elect Engn Ranchi Bihar India

ISBN: (纸本)9788132222088;9788132222071

The modeling of electrical machine is approached as a system optimization, more than a simple machine sizing. Hence wide variety of designs are available and the task of comparing the different options can be very difficult. A number of parameters are involved in the design optimization of the induction motor and the performance relationship between the parameters also is implicit. In this paper, a multi-objective problem is considered in which three phase squirrel cage induction motor (SCIM) has been designed subject to the efficiency and power density as objectives. The former is maximized where the latter is minimized simultaneously considering various constraints. Three single objective methods such as Tabu Search (TS), Simulated Annealing (SA) and Genetic Algorithm (GA) is used for comparing the Pareto solutions. Performance comparison of techniques is done by performing different numerical experiments. The result shows that NSGA-II outperforms other three for the considered test cases.

关键词： multi-objective optimization Induction motors multi-objective evolutionary algorithms Single objective evolutionary algorithm

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Hybrid multi-objective Greenhouse Crop Optimization using the Parallel Island Model

Hybrid Multi-Objective Greenhouse Crop Optimization using th...

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7th International Conference on Engineering Computational Technology

作者： Marquez, A. L. Gil, C. Manzano-Agugliaro, F. Montoya, F. G. Montoya, M. G. Banos, R. 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)9781905088416

multi-objective evolutionary algorithms are commonly used to solve complex problems. It is possible to improve the quality of the solutions they can produce by means of employing advanced parallelization techniques. This paper solves a land management problem by using the Island Parallel Model with the purpose of expanding the number of objectives to optimize by using several isolated populations exploring two dimensional search spaces and migrating the best subjects to nearby populations that are exploring a search space with a different objective. The goal is to fuel the generation of solutions that are optimal in both search spaces to generate a three objective pareto front that is the solution of a larger problem.

关键词： multi-objective evolutionary algorithms island parallel model threads greenhouse crops resource optimization

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Adaptive Pareto differential evolution and its parallelization

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5th International Conference on Parallel Processing and Applied Mathematics

作者： Zaharie, D Petcu, D W Univ Timisoara Dept Comp Sci Timisoara 300223 Romania

ISBN: (纸本)3540219463

An adaptive Pareto differential evolution algorithm for multi-objective optimization is proposed. Its effectiveness on approximating the Pareto front is compared with that of SPEA[9] and of SPDE[2]. A parallel implementation, based on an island model with a random connection topology, is also analyzed. The parallelization efficiency derives from the simple migration strategy. Numerical tests were performed on a cluster of workstations.

关键词： multi-objective evolutionary algorithms Pareto front parameter adaptation island model parallel computing PC cluster

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