@inproceedings{5e8e146f41934b7ba47120860ee9e23c,
title = "Differential evolution algorithms under multi-population strategy",
abstract = "A differential evolution (DE) algorithm is an evolutionary algorithm for optimization problems over a continuous domain. To solve high dimensional global optimization problems, this work investigates the performance of differential evolution algorithms under a multi-population strategy. The original DE algorithm generates an initial set of suitable solutions. The multi population strategy divides the set into several subsets. These subsets evolve independently and connect with each other according to the DE algorithm. This helps in preserving the diversity of the initial set. Furthermore, a comparison of combination of different mutation techniques on several optimization algorithms is studied to verify their performance. Finally the computational results on eleven well-know benchmark optimization functions, reveal some interesting relationship between the number of subpopulations and performance of the DE.",
keywords = "Differential evolution, crossover, fitness value, mutation, optimization, population",
author = "Ishani Chatterjee and Mengchu Zhou",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 26th Wireless and Optical Communication Conference, WOCC 2017 ; Conference date: 07-04-2017 Through 08-04-2017",
year = "2017",
month = may,
day = "15",
doi = "10.1109/WOCC.2017.7928972",
language = "English (US)",
series = "2017 26th Wireless and Optical Communication Conference, WOCC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2017 26th Wireless and Optical Communication Conference, WOCC 2017",
address = "United States",
}