Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation

Abstract

Deriving optimal operation policies for multi-reservoir systems is a complex engineering problem. It is necessary to employ a reliable technique to efficiently solving such complex problems. In this study, five recently-introduced robust evolutionary algorithms (EAs) of Harris hawks optimization algorithm (HHO), seagull optimization algorithm (SOA), sooty tern optimization algorithm (STOA), tunicate swarm algorithm (TSA) and moth swarm algorithm (MSA) were employed, for the first time, to optimal operation of Halilrood multi-reservoir system. This system includes three dams with parallel and series arrangements simultaneously. The results of mentioned algorithms were compared with two well-known methods of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The objective function of the optimization model was defined as the minimization of total deficit over 223 months of reservoirs operation. Four performance criteria of reliability, resilience, vulnerability and sustainability were used to compare the algorithms’ efficiency in optimization of this multi-reservoir operation. It was observed that the MSA algorithm with the best value of objective function (6.96), the shortest CPU run-time (6738 s) and the fastest convergence rate (< 2000 iterations) was the superior algorithm, and the HHO algorithm placed in the next rank. The GA, and the PSO were placed in the middle ranks and the SOA, and the STOA placed in the lowest ranks. Furthermore, the comparison of utilized algorithms in terms of sustainability index indicated the higher performance of the MSA in generating the best operation scenarios for the Halilrood multi-reservoir system. The application of robust EAs, notably the MSA algorithm, to improve the operation policies of multi-reservoir systems is strongly recommended to water resources managers and decision-makers.

Optimization of hydropower energy generation by 14 robust evolutionary algorithms

“…The position of each particle is corrected by taking small movements in the neighborhood of the global optimum. By adopting the correct sequence of these two phases, it is possible to lead particles towards the global optimum 37 .…”

“…A new generation is formed by selecting some parents and some offspring according to their fitness values, and by rejecting others to keep the population size constant. After the predetermined number of generations is performed, the algorithm converges to the best chromosome, which hopefully represents the optimal solution or may be a near-optimal solution of the problem 37 .…”

Investigating dam reservoir operation optimization using metaheuristic algorithms

“…Therefore, dam reservoirs play a very important role in current civilizations, hence their operation needs to be managed effectively. Policies for reservoir dam operation are generally determined in order to set the course towards achieving desired objectives such as the maximization of hydropower generation; and the minimization of operation costs, risks, and deficiencies particularly in terms of water release and storage (Asadieh and Afshar 2019;Ehteram et al 2018;Motlagh et al 2021;Sharifi et al 2021). As there are often various different objectives in dam reservoir operations, optimization is required to search for optimum solutions that simultaneously maximizes benefits on all fronts, given a set of objective functions and constraints (Chong et al 2021).…”

“…Meanwhile, limited studies have also been found on the usage of HHO in dam reservoir operation optimization. Sharifi et al (2021) compared the performance of several evolutionary-based models, namely HHO, MSA, GA, PSO, seagull optimization algorithm (SOA), sooty tern optimization algorithm (STOA), and tunicate swarm algorithm (TSA), in optimizing the operation of the Halilrood multi-reservoir system. It was found that HHO was among the top performing models in the study in terms of objective value function, CPU run-time, and convergence rate.…”

“…The effectiveness of dam reservoir operation optimization depends heavily on the metaheuristic utilized. By implementing more powerful and suitable metaheuristics, dam reservoir operators will be able to improve their ability in maximizing hydropower generation profit while minimizing costs, risks, and deficiencies (Asadieh and Afshar 2019;Ehteram et al 2018;Motlagh et al 2021;Sharifi et al 2021). The present study was hence motivated by the importance of continuous research and testing in order to understand the applicability of different metaheuristics and possibly discover better performing metaheuristics for a particular task, which in this case is the optimization of dam reservoir operation.…”