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A novel hybrid optimization model to determine optimum water resources for water supply of residential areas

数学优化 克隆选择算法 启发式 计算机科学 供水 理论(学习稳定性) 混合算法(约束满足) 算法 数学 环境科学 人工智能 人工免疫系统 环境工程 机器学习 随机规划 约束规划 约束逻辑程序设计
作者
Miraç Eryiğit
出处
期刊:Journal of water process engineering [Elsevier BV]
卷期号:55: 104087-104087
标识
DOI:10.1016/j.jwpe.2023.104087
摘要

In this study, a new hybrid model was improved by combining heuristic and numerical optimization algorithms to decide on optimum water resources based on their costs in the water supply. The purpose of the hybrid model is to reach a best result in the shortest time by simultaneously searching global and local minimums. Therefore, the steepest descent (SD) algorithm (numerical optimization method) was embedded in the classical modified clonal selection algorithm (the classical modified Clonalg) (one of artificial immune systems, heuristic optimization technique). This hybridization allows the SD algorithm to search local minimums while the classical modified Clonalg is searching a global minimum. The hybrid optimization model was applied to the cost objective function depending on distances and piezometric head differences between the water resources and destination. A scenario consists of five hypothetical water resources and one residential area/settlement. Herein, the aim is to satisfy the water demand of the residential area with a minimum cost from the water resources. The cost objective function was also minimized by the regular model (the classical model) according to the scenario, and their results were compared. Both models were run ten times for testing their stabilities. According to the results, the hybrid model is better than the regular model in terms of run time and stability. The hybrid model found a minimum cost for the water supply in a shorter time (in half) in comparison with the regular model in all runs.
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