Paper Info
Title
Exploiting diversity in an asynchronous migration model for distributed differential evolution.
Abstract
In this paper an improved version of a general-purpose asynchronous adaptive multi-population model for distributed Differential Evolution algorithm is investigated. Specifically, in addition to an asynchronous mechanism for a multi-population recombination employed to exchange information, the distributed algorithm is endowed also with an innovative mechanism able to exploit diversity for the selection of the subpopulations involved in the asynchronous communication. Moreover the model is provided with a specific updating scheme to randomly update the control parameter values. The asynchronous migration mechanism and the adaptive procedure allow reducing the number of control parameters to be set and tuned in the distributed model respectively. The proposed distributed algorithm has been tested on the benchmarks of the CEC2016 real parameter single objective competition without adopting any specific mechanism opportunely tailored for solving such test problems. The results compared with the basic version of the distributed algorithm reveal an improvement in the performance in most of the considered benchmarks.
Year
DOI
Venue
2017
10.1145/3067695.3084217
GECCO (Companion)
Keywords
Field
DocType
Distributed Differential Evolution, asynchronous model, performance evaluation
Asynchronous communication,Distributed element model,Computer science,Distributed design patterns,Differential evolution,Exploit,Distributed algorithm,Software,Artificial intelligence,Differential evolution algorithm,Machine learning,Distributed computing
Conference
Citations 
PageRank 
References 
1
0.35
39
Authors
4
Name
Order
Citations
PageRank
Ivanoe De Falco124234.58
Antonio Della Cioppa214120.70
Umberto Scafuri311616.33
Ernesto Tarantino436142.45