@inbook{7fbce932b2f24b0e96222afc23a77ad2,
title = "Hybrid Generalized Normal Distribution Optimization with Sine Cosine Algorithm for Global Optimization",
abstract = "This paper proposes two hybrid versions of the generalized normal distribution optimization (GNDO) and sine cosine algorithm (SCA) for global optimization. The proposed hybrid methods combine the excellent characteristics of the GNDO and SCA algorithms to enhance the exploration and exploitation behaviors. Moreover, an additional weight parameter is introduced to further improve the search ability of the hybrid methods. The proposed methods are tested with 23 mathematical optimization problems. Our results reveal that the proposed hybrid method was very competitive compared to the other metaheuristic algorithms.",
keywords = "Generalized normal distribution optimization, Optimization, Sine cosine algorithm",
author = "Jingwei Too and Sadiq, {Ali Safaa} and Hesam Akbari and Mong, {Guo Ren} and Seyedali Mirjalili",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.",
year = "2022",
doi = "10.1007/978-981-19-2948-9_4",
language = "English",
series = "Lecture Notes on Data Engineering and Communications Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "35--42",
booktitle = "Lecture Notes on Data Engineering and Communications Technologies",
}