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Weighted Stress Function Method for Multiobjective Evolutionary Algorithm Based on Decomposition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10173)

Abstract

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a well established state-of-the-art framework. Major concerns that must be addressed when applying MOEA/D are the choice of an appropriate scalarizing function and setting the values of main control parameters. This study suggests a weighted stress function method (WSFM) for fitness assignment in MOEA/D. WSFM establishes analogy between the stress-strain behavior of thermoplastic vulcanizates and scalarization of a multiobjective optimization problem. The experimental results suggest that the proposed approach is able to provide a faster convergence and a better performance of final approximation sets with respect to quality indicators when compared with traditional methods. The validity of the proposed approach is also demonstrated on engineering problems.

Keywords

Quality Indicator Scalarizing Function Pareto Front Multiobjective Optimization Problem Multiobjective Evolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work has been supported by FCT - Fundação para a Ciência e Tecnologia in the scope of the project: PEst-OE/EEI/UI0319/2014.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Polymer EngineeringUniversity of MinhoGuimarãesPortugal

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