A novel adaptive model for a recently devised distributed Differential Evolution algorithm is introduced. The distributed algorithm, following the stepping-stone model, is characterized by a migration model inspired by the phenomenon known as biological invasion. The adaptive model is endowed with three updating schemes to randomly set the mutation and the crossover parameters. These schemes are here tied to the migration and are guided by a performance measure between two consecutive migrations. The proposed adaptive model is tested on a set of classical benchmark functions over the different setting schemes. To evaluate its performance, the model is compared against the original non-adaptive version with a fixed parameter setting, and against a well-known distributed Differential Evolution algorithm equipped with the same schemes for the control parameter updating. The experimental study shows that the method results in high effectiveness in terms of solutions detected and convergence speed on most of the benchmark problems and for the majority of the setting schemes investigated. Finally, to further estimate its effectiveness, the proposed approach is also compared with several state-of-the-art Differential Evolution frameworks endowed with different randomized or self-adaptive parameter setting strategies. This comparison shows that our adaptive model allows obtaining the best performance in most of the tests studied.

An adaptive invasion-based model for distributed Differential Evolution

DELLA CIOPPA, Antonio;
2014

Abstract

A novel adaptive model for a recently devised distributed Differential Evolution algorithm is introduced. The distributed algorithm, following the stepping-stone model, is characterized by a migration model inspired by the phenomenon known as biological invasion. The adaptive model is endowed with three updating schemes to randomly set the mutation and the crossover parameters. These schemes are here tied to the migration and are guided by a performance measure between two consecutive migrations. The proposed adaptive model is tested on a set of classical benchmark functions over the different setting schemes. To evaluate its performance, the model is compared against the original non-adaptive version with a fixed parameter setting, and against a well-known distributed Differential Evolution algorithm equipped with the same schemes for the control parameter updating. The experimental study shows that the method results in high effectiveness in terms of solutions detected and convergence speed on most of the benchmark problems and for the majority of the setting schemes investigated. Finally, to further estimate its effectiveness, the proposed approach is also compared with several state-of-the-art Differential Evolution frameworks endowed with different randomized or self-adaptive parameter setting strategies. This comparison shows that our adaptive model allows obtaining the best performance in most of the tests studied.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4366653
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