Fusing feasible search space into PSO for multi-objective cascade reservoir optimization

This study proposes a multi-objective optimization model of two cascade reservoirs in the Upper Yellow River basin for increasing social well-beings in general while simultaneously mitigating ice/flood threats. We first develop a strategy of dimensionality reduction and constraint transformation to largely diminish the complexity of the optimization system and next propose a novel search method that fuses a Feasible Search Space (FSS) into the Particle Swarm Optimization (PSO) algorithm, i.e. FSS-PSO, to effectively solve the optimization problem. To investigate the applicability and effectiveness of the proposed method, this study compares the FSS-PSO model with historical operation. The results indicate that the proposed model produces much better performances in all the objectives than historical operation. To assess the superiority and efficiency of the proposed FSS-PSO, the classical PSO and the Chaos Particle Swarm Optimization (CPSO) are also implemented to compare their computation time and convergence ater and sediment regulation rates. The results demonstrate that the FSS-PSO improves the efficiency of the PSO and the CPSO by 72% and 55% accordingly and the convergence rate of the FSS-PSO is the fastest among the three algorithms. The results suggest that the proposed dimensionality-reduction strategy coupled with the FSS-PSO algorithm is a promising tool for water resources management under multi-objective joint reservoir operation and the proposed method could be easily implemented in the context of multi-objective optimization. © 2016 Elsevier B.V. All rights reserved.