A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Management of a Water Pumping Schedule by an HGMA Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0002-7660-5618
Optimizing pumping schedules is crucial for the effective management of drinking water pumping stations. Various methods have been employed to address this problem, ranging from simple exact methods to more recent metaheuristic methods. In this study, we propose a novel optimization model, the hybrid genetic modification algorithm (HGMA), which enables multi-objective optimization of pumping schedules with respect to energy consumption and pumping costs. The model ensures that the optimization is carried out under constraints that maintain the boundary conditions of the distribution tank. The principle of the HGMA model is inspired by the genetic modification technique used in organisms. Comparing the HGMA model's performance with that of genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) models reveals its superior performance and efficiency on all levels.
Management of a Water Pumping Schedule by an HGMA Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0002-7660-5618
Optimizing pumping schedules is crucial for the effective management of drinking water pumping stations. Various methods have been employed to address this problem, ranging from simple exact methods to more recent metaheuristic methods. In this study, we propose a novel optimization model, the hybrid genetic modification algorithm (HGMA), which enables multi-objective optimization of pumping schedules with respect to energy consumption and pumping costs. The model ensures that the optimization is carried out under constraints that maintain the boundary conditions of the distribution tank. The principle of the HGMA model is inspired by the genetic modification technique used in organisms. Comparing the HGMA model's performance with that of genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) models reveals its superior performance and efficiency on all levels.
Management of a Water Pumping Schedule by an HGMA Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0002-7660-5618
Optimizing pumping schedules is crucial for the effective management of drinking water pumping stations. Various methods have been employed to address this problem, ranging from simple exact methods to more recent metaheuristic methods. In this study, we propose a novel optimization model, the hybrid genetic modification algorithm (HGMA), which enables multi-objective optimization of pumping schedules with respect to energy consumption and pumping costs. The model ensures that the optimization is carried out under constraints that maintain the boundary conditions of the distribution tank. The principle of the HGMA model is inspired by the genetic modification technique used in organisms. Comparing the HGMA model's performance with that of genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) models reveals its superior performance and efficiency on all levels.
Management of a Water Pumping Schedule by an HGMA Optimization Algorithm
Iran J Sci Technol Trans Civ Eng
Bouach, Ahcene (author) / Benmamar, Saadia (author)
2023-12-01
13 pages
Article (Journal)
Electronic Resource
English
Optimization of pumping schedule based on forecasting the hourly water demand in Seoul
| Online Contents | 2007
Computer‐generated optimal pumping schedule
| Wiley | 1995
Pumping schedule assignment using chromatic graphs to reduce groundwater pumping energy consumption
| DOAJ | 2022
CONCRETE PUMPING - A new home for the Grizzlies - Pumping keeps to schedule in Memphis
Online Contents | 2003
| Taylor & Francis Verlag | 2014