A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms

Date
2014-11
Authors
Iacca, Giovanni
Neri, Ferrante
Caraffini, Fabio
Suganthan, Ponnuthurai Nagaratnam
Journal Title
Journal ISSN
ISSN
Volume Title
Publisher
Springer Berlin Heidelberg
Peer reviewed
Yes
Abstract
The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework cooperatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts to a given problem and selects those search logics that appear to be the most appropriate to quickly detect high quality solutions. The resulting algorithm, namely Ensemble of Parameters and Strategies Differential Evolution empowered by Local Search (EPSDE-LS), is evaluated on multiple testbeds and dimensionality values. Numerical results show that the proposed EPSDE-LS robustly displays a very good performance in comparison with some of the state-of-the-art algorithms.
Description
The file attached to this record is the author's final peer reviewed version. The publisher's final version can be found by following the DOI link.
Keywords
Differential evolution, Ensemble, Adaptive algorithms, Optimisation, Meta-heuristics, Memetic Computing
Citation
Iacca, G., Neri, F., Caraffini, F. and Suganthan, P. N. (2014) A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms. In Applications of Evolutionary Computation: 17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers, pp. 615-626
Research Institute
Institute of Artificial Intelligence (IAI)