Multipurpose reservoir operation using particle swarm optimization

Abstract

This paper presents an efficient and reliable swarm intelligence-based approach, namely elitist-mutated particle swarm 
optimization (EMPSO) technique, to derive reservoir operation policies for multipurpose reservoir systems. Particle swarm 
optimizers are inherently distributed algorithms, in which the solution for a problem emerges from the interactions between 
many simple individuals called particles. In this study the standard particle swarm optimization (PSO) algorithm is further 
improved by incorporating a new strategic mechanism called elitist-mutation to improve its performance. The proposed approach 
is first tested on a hypothetical multireservoir system, used by earlier researchers. EMPSO showed promising results, when 
compared with other techniques. To show practical utility, EMPSO is then applied to a realistic case study, the Bhadra reservoir 
system in India, which serves multiple purposes, namely irrigation and hydropower generation. To handle multiple objectives of 
the problem, a weighted approach is adopted. The results obtained demonstrate that EMPSO is consistently performing better than 
the standard PSO and genetic algorithm techniques. It is seen that EMPSO is yielding better quality solutions with less number 
of function evaluations.