Super-fit control adaptation in memetic differential evolution frameworks

De Montfort University Open Research Archive

Show simple item record Caponio, A. en Neri, Ferrante en Tirronen, Ville en 2012-08-10T13:57:14Z 2012-08-10T13:57:14Z 2009-07
dc.identifier.citation Caponio, A., Neri, F. and Tirronen, V. (2009) Super-fit control adaptation in memetic differential evolution frameworks. Soft Computing-A Fusion of Foundations, Methodologies and Applications, 13 (8-9), pp 811-831 en
dc.identifier.issn 1432-7643
dc.description.abstract This paper proposes the super-fit memetic differential evolution (SFMDE). This algorithm employs a differential evolution (DE) framework hybridized with three meta-heuristics, each having different roles and features. Particle Swarm Optimization assists the DE in the beginning of the optimization process by helping to generate a super-fit individual. The two other meta-heuristics are local searchers adaptively coordinated by means of an index measuring quality of the super-fit individual with respect to the rest of the population. The choice of the local searcher and its application is then executed by means of a probabilistic scheme which makes use of the generalized beta distribution. These two local searchers are the Nelder mead algorithm and the Rosenbrock Algorithm. The SFMDE has been tested on two engineering problems; the first application is the optimal control drive design for a direct current (DC) motor, the second is the design of a digital filter for image processing purposes. Numerical results show that the SFMDE is a flexible and promising approach which has a high performance standard in terms of both final solutions detected and convergence speed. en
dc.language.iso en en
dc.publisher Springer en
dc.title Super-fit control adaptation in memetic differential evolution frameworks en
dc.type Article en
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en

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