Bala, I.Yadav, A.Kim, J.H.2024-04-112024-04-112024Evolutionary Intelligence, 2024; 17(4):2981-30131864-59091864-5917https://hdl.handle.net/2440/140549Published online: 22 March 2024The Comprehensive Learning Gravitational Search Algorithm (CLGSA) has demonstrated its efectiveness in solving continuous optimization problems. In this research, we extended the CLGSA to tackle NP-hard combinatorial problems and introduced the Discrete Comprehensive Learning Gravitational Search Algorithm (D-CLGSA). The D-CLGSA framework incorporated a refned position and velocity update scheme tailored for discrete problems. To evaluate the algorithm's efciency, we conducted two sets of experiments. Firstly, we assessed its performance on a diverse range of 24 benchmarks encompassing unimodal, multimodal, composite, and special discrete functions. Secondly, we applied the D-CLGSA to a practical optimization problem involving water distribution network planning and management. The D-CLGSA model was coupled with the hydraulic simulation solver EPANET to identify the optimal design for the water distribution network, aiming for cost-efectiveness. We evaluated the model's performance on six distribution networks, namely Two-loop network, Hanoi network, New-York City network, GoYang network, BakRyun network, and Balerma network. The results of our study were promising, surpassing previous studies in the feld. Consequently, the D-CLGSA model holds great potential as an optimizer for economically and reliably planning and managing water networks.en© The Author(s) 2024. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Meta-heuristic algorithm; Comprehensive learning gravitational search algorithm; Binary space; Global optimization benchmarks; Water network systemJournal article10.1007/s12065-024-00922-x2024-03-25688801Bala, I. [0000-0002-7215-5269]