Pareto‐based continuous evolutionary algorithms for multiobjective optimization

Pareto‐based continuous evolutionary algorithms for multiobjective optimization In an attempt to solve multiobjective optimization problems, many traditional methods scalarize an objective vector into a single objective by a weight vector. In these cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands a user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto‐optimal points, instead of a single point. In this paper, Pareto‐based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. These algorithms are based on Continuous Evolutionary Algorithms, which were developed by the authors to solve single‐objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche‐formation method for fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto‐optimal tradeoff surface. Finally, the validity of this method has been demonstrated through some numerical examples. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering Computations Emerald Publishing

Pareto‐based continuous evolutionary algorithms for multiobjective optimization

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Publisher
Emerald Publishing
Copyright
Copyright © 2002 MCB UP Ltd. All rights reserved.
ISSN
0264-4401
DOI
10.1108/02644400210413649
Publisher site
See Article on Publisher Site

Abstract

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize an objective vector into a single objective by a weight vector. In these cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands a user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto‐optimal points, instead of a single point. In this paper, Pareto‐based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. These algorithms are based on Continuous Evolutionary Algorithms, which were developed by the authors to solve single‐objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche‐formation method for fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto‐optimal tradeoff surface. Finally, the validity of this method has been demonstrated through some numerical examples.

Journal

Engineering ComputationsEmerald Publishing

Published: Feb 1, 2002

Keywords: Optimization; Algorithms

References

  • An overview of evolutionary algorithms for parameter optimization
    Baek, T.; Schwefel, H.‐P.
  • Multi‐objective genetic algorithms: problem difficulties and construction of test problems
    Deb, K.
  • Inelastic constitutive parameter identification using an evolutionary algorithm with constitutive individuals
    Furukawa, T.; Yagawa, G.
  • Crack identification using hybrid neuro‐genetic technique
    Suh, M.W.; Shim, M.B.; Kim, M.Y.

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