Efficiency Criteria as a Solution to the Uncertainty in the Choice of Population Size in Population-Based Algorithms Applied to Water Network Optimization

作     者：Mora-Melia, Daniel Gutierrez-Bahamondes, Jimmy H. Iglesias-Rey, Pedro L. Javier Martinez-Solano, F. 

作者机构：Univ Talca Fac Ingn Dept Ingn & Gest Construcc Talca 3340000 Chile Univ Politecn Valencia Dept Ingn Hidraul & Medio Ambiente E-46022 Valencia Spain 

出 版 物：《WATER》 (水)

年 卷 期：2016年第8卷第12期

页      面：583页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 081501[工学-水文学及水资源] 0815[工学-水利工程] 

基　　金：Program Initiation into research of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt)  Chile 

主　　题：population-based algorithms pipe-sizing problem water distribution networks optimization population size 

摘      要：Different Population-based Algorithms (PbAs) have been used in recent years to solve all types of optimization problems related to water resource issues. However, the performances of these techniques depend heavily on correctly setting some specific parameters that guide the search for solutions. The initial random population size P is the only parameter common to all PbAs, but this parameter has received little attention from researchers. This paper explores P behaviour in a pipe-sizing problem considering both quality and speed criteria. To relate both concepts, this study applies a method based on an efficiency ratio E. First, specific parameters in each algorithm are calibrated with a fixed P. Second, specific parameters remain fixed, and the initial population size P is modified. After more than 600,000 simulations, the influence of P on obtaining successful solutions is statistically analysed. The proposed methodology is applied to four well-known benchmark networks and four different algorithms. The main conclusion of this study is that using a small population size is more efficient above a certain minimum size. Moreover, the results ensure optimal parameter calibration in each algorithm, and they can be used to select the most appropriate algorithm depending on the complexity of the problem and the goal of optimization.