Abstract
In engineering and scientific disciplines, there are extensive Optimization Application Problems (OAPs) such as economic dispatch, structural design, and water resources. One of the major OAPs is the operation of dams and reservoirs to minimize the gap between water supply for irrigation and demand patterns such as hydropower generation. Drawing optimal operation for dams and reservoirs is often categorized as discontinuity, multimodality, non-differentiability and non-convexity. Classical mathematical programming-based methods for optimization might be inappropriate or unrealizable in drawing optimal operation rules for dam and reservoir operation. During the last two decades, new optimization methods-based on nature-inspired meta-heuristic algorithms (MHAs) have motivated hydrologists to investigate MHAs as better alternative optimization tools for identifying the optimal dam and reservoir operation rules. To solve the dam and reservoir-optimization applications better, this review presents the past, present, and prospective research directions using MHAs. The problem of dam and reservoir optimization requires a fundamental shift of focus towards enhancing not only the problem formulation and decomposition but also the computational efficiency of MHAs.
Highlights
In the past 20 years, many researchers applied various nature-inspired meta-heuristic algorithms (MHAs) to different classes of dam and reservoir water systems
The results showed that the PARTICLE SWARM OPTIMIZATION (PSO) has a faster convergence speed than a GENETIC ALGORITHM (GA)
Facing a problem with many local optima, the meta-heuristic algorithm that has poor exploitation will find it hard to get to the global optimum [66]. It can be seen based on the previous study, algorithms such as water cycle algorithm (WCA), CELLULAR AUTOMATA (CA), Honeybees Mating Optimization (HBMO), BAT ALGORITHM (BA), BIOGEOGRAPHY-BASED OPTIMIZATION (BBO), PSO, ANT COLONY OPTIMIZATION (ACO), and FIREFLY ALGORITHM (FA) could solve reservoir optimization problems even though they are either weak in exploitation or exploration search ability
Summary
In the past 20 years, many researchers applied various nature-inspired MHAs to different classes of dam and reservoir water systems. (e.g., single reservoir, single-purpose; single reservoir, multipurpose; or multi-reservoir, multipurpose). To step further in this area of research, it is necessary to understand the interrelationship between the reservoir water system’s features of the case study being optimized, the searching mathematical procedure of the optimization algorithm, and the performance of the algorithm. This insightful assessment could motivate hydrologists to go beyond only knowing the performance indices of algorithm or case study to attain the near-full understanding that might be more general to be applicable for betterment selection of the compatibility between the optimization algorithm(s) for a certain case study In the such reservoir systems need a further adaptation of the algorithm’s mathematical technique and the need for them to be combined or hybridized with other MHAs to make such a special reservoir water system [5]–[7].
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