@inproceedings{92a8facef91e4190b85aac00671663e2,
title = "Improved United Multi-Operator Algorithm for Solving Optimization Problems",
abstract = "Although many evolutionary algorithms (EAs) have successfully solved different optimization problems, no single EA has consistently been the best for all these problems. During the last decade, to alleviate this limitation, many proposals which utilize multiple EAs in a single algorithmic framework, called multi-methods or multi-operators, have been introduced. However, there is still room to enhance their performance. In this paper, an improved variant of a united multi-operator algorithm is introduced with few improvements that are capable of providing a balance between diversification and intensification properties during the optimization. The proposed algorithm is tested on the CEC2017 unconstrained benchmark problems, with the results revealing that the proposed algorithm is capable of producing high quality solutions compared with those of state-of-the-art algorithms.",
keywords = "evolutionary algorithms, multi-method, multi-operator",
author = "Sallam, {Karam M.} and Elsayed, {Saber M.} and Sarker, {Ruhul A.} and Essam, {Daryl L.}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Congress on Evolutionary Computation, CEC 2018 ; Conference date: 08-07-2018 Through 13-07-2018",
year = "2018",
month = sep,
day = "28",
doi = "10.1109/CEC.2018.8477759",
language = "English",
isbn = "9781509060184",
series = "2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1--8",
editor = "{Von Zuben}, Fernando and Gary Yen and Hisao Ishibuchi",
booktitle = "2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings",
address = "United States",
}