Abstract
Firefly algorithm (FA) is an efficient swarm intelligence optimization technique, which has been used to solve many engineering optimization problems. In this paper, we present a new FA (called NFA) variant for demand estimation of water resources in Nanchang city of China. The performance of the standard FA highly depends on its control parameters. To tackle this issue, a dynamic step factor strategy is proposed. In NFA, the step factor is not fixed and it is dynamically updated during the search process. Three models in different forms (linear, exponential and hybrid) are developed based on the structure of social and economic conditions. Water demand in Nanchang city from 2003 to 2015 is considered as a case study. The data from 2003 to 2012 is used for finding the optimal weights, and the rest data (2013–2015) is for testing the models. Simulation results show that three FA variants can achieve promising performance. Our proposed NFA outperforms the standard FA and memetic FA (MFA), and the prediction accuracy is up to 97.91%.
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