Multiobjective Differential Evolution Algorithm with Self-Adaptive Learning Process

  • Andrzej Cichoń
  • Ewa Szlachcic
Part of the Studies in Computational Intelligence book series (SCI, volume 378)


This chapter presents an efficient strategy for self-adaptation mechanisms in a multiobjective differential evolution algorithm. The algorithm uses parameters adaptation and operates with two differential evolution schemes. Also, a novel DE mutation scheme combined with a transversal individual idea is introduced to support the convergence rate of the algorithm. The performance of the proposed algorithm, named DEMOSA, is tested on a set of benchmark problems. The numerical results confirm that the proposed algorithm performs considerably better than the one with simple DE scheme in terms of computational cost and quality of the identified nondominated solutions sets.


Differential Evolution Pareto Front Multiobjective Optimization Differential Evolution Algorithm Multiobjective Optimization Problem 
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.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrzej Cichoń
    • 1
  • Ewa Szlachcic
    • 1
  1. 1.Institute of Computer Engineering, Control and RoboticsWroclaw University of TechnologyWroclawPoland

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