Integrating Cross-Dominance Adaptation in Multi-Objective Memetic Algorithms

  • Andrea Caponio
  • Ferrante Neri
Part of the Studies in Computational Intelligence book series (SCI, volume 171)

Abstract

This chapter proposes a novel adaptive memetic approach for solving multi-objective optimization problems. The proposed approach introduces the novel concept of crossdominance and employs this concept within a novel probabilistic scheme which makes use of the Wigner distribution for performing coordination of the local search. Thus, two local searchers are integrated within an evolutionary framework which resorts to an evolutionary algorithm previously proposed in literature for solving multi-objective problems. These two local searchers are a multi-objective version of simulated annealing and a novel multi-objective implementation of the Rosenbrock algorithm.

Numerical results show that the proposed algorithm is rather promising and, for several test problems, outperforms two popular meta-heuristics present in literature. A realworld application in the field of electrical engineering, the design of a control system of an electric motor, is also shown. The application of the proposed algorithm leads to a solution which allows successful control of a direct current motor by simultaneously handling the conflicting objectives of the dynamic response.

Keywords

Local Search Multiobjective Optimization Memetic Algorithm Decision Space Direct Current Motor 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Andrea Caponio
    • 1
  • Ferrante Neri
    • 2
  1. 1.Department of Electrotechnics and ElectronicsTechnical University of BariItaly
  2. 2.Department of Mathematical Information TechnologyUniversity of JyväskyläFinland

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