Sustainable Conjunctive Water Use Modeling Using Dual Fitness Particle Swarm Optimization Algorithm

Abstract

In any meta-heuristic algorithm, each search agent must move to the high-fitness areas in the search space while preserving its diversity. At first glance, there is no relationship between fitness and diversity, as two key factors to be considered in selecting a guide for the solutions. In other words, each of these factors must be evaluated in its specific and independent way. Since the independent ways to evaluate the fitness and diversity usually make any meta-heuristic consider these factors disproportionately to choose the guides, the solutions’ movements may be unbalanced. In this paper, we propose a novel version of the Particle Swarm Optimization (PSO) algorithm, named Dual Fitness PSO (DFPSO). In this algorithm, not only fitness and diversity of the particles are properly evaluated, but also the abilities to evaluate these features are integrated to avoid the abovementioned problem in determining the global guide particles. After verification of the DFPSO via applying them to several benchmark functions, it is applied to solve a real-world optimal conjunctive water use management problem. The objective is minimizing shortages in meeting irrigation water demands under several climatic conditions. The optimal results suggest that while the water demands are desirably met, the cumulative groundwater level (GWL) drawdown is highly decreased to help maintain the sustainability of the aquifer, demonstrating the high efficiency of the DFPSO to also handle the practical engineering problems.