An improved MOPSO algorithm for multi-objective optimization of reservoir operation under climate change

Gradually, the previously proposed water resource management schemes and reservoir operating policies adjusted to the historically experienced climatic conditions are losing their validity and efficacy, urging building up the models compatible with the likely climatic change conditions at the future. This paper aims at optimizing the reservoir operation under climate change conditions targeting the objectives including (1) minimizing the shortages in meeting the reservoir downstream water demands and (2) maximizing the sustainability of the reservoir storage. For evaluating the effects of the climate change, six general circulation models (GCMs) built up under the representative concentration pathway (RCP4.5) emission scenario are adopted and utilized to predict the climate variables over a 30-year planning period. To solve this problem, an improved version of our recently proposed fuzzy multi-objective particle swarm optimization (f-MOPSO) algorithm, named f-MOPSO-II, is proposed. The f-MOPSO takes a novel approach to handle multi-objective nature of the optimization problems. In this approach, the common concept of “diversity” is replaced with “extremity,” to choose the better guides of the search agents in the algorithm. The f-MOPSO-II is based on the f-MOPSO. However, it is aimed at simultaneously mitigating the f-MOPSO computational complexity and enhancing the quality of the final results presented by this algorithm. The results obtained by the f-MOPSO-II were then compared with those yielded by the popular non-dominated sorting genetic algorithm-II (NSGA-II). As the results suggest, the f-MOPSO-II is capable of simultaneously meeting the water demands and holding the reservoir storage sustainable, much better than the NSGA-II.