Evolutionary algorithms (EAs) have been widely used to search for optimal strategies for the planning and management of water resources systems, particularly reservoir operation. This study provides a comprehensive diagnostic assessment of state of the art of the non-animal-inspired EA applications to reservoir optimization. This type of EAs does not mimic biologic traits and group strategies of animal (wild) species. A search of pertinent papers was applied to the journal citation reports (JCRs). A bibliometric survey identified 14 pertinent non-animal-inspired EAs, such as the genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE) algorithms, most of which have a number of modified versions. The characteristics of non-animal-inspired EAs and their modified versions were discussed to identify the difference between EAs and how each EA was improved. Additionally, the type of application of non-animal-inspired EAs to different case studies was investigated, and comparisons were made between the performance of the applied EAs in the studied literature. The survey revealed that the GA is the most frequently applied algorithm, followed by the DE algorithm. Non-animal-inspired EAs are superior to the classical methods of reservoir optimization (e.g., the non-linear programming and dynamic programming) due to faster convergence, diverse solution space, and efficient objective function evaluation. Several non-animal-inspired EAs of recent vintage have been shown to outperform the classic GA, which was the first evolutionary algorithm applied to reservoir operation.

Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO's applications and performance analysis in waterresources engineering optimization problems. Our literature review revealed 33 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall-runoff modelling, water quality modelling, and groundwater modelling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., Simulated Annealing, Differential Evolution, Genetic Algorithm, and Shark Algorithm) and mathematical methods (e.g., Support Vector Machine and Differential Dynamic Programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.

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Successful operation of reservoir systems to guarantee the optimal use of available water resources has been the subject of many studies. The advent and applications of evolutionary algorithms (EAs) in the field of reservoir operation have led to significant advances in our capacity to improve the planning and management of complex reservoir systems. This study reports a review of the applications of animal‐inspired EAs to reservoir operation optimization selected among a large number of available papers in this area of research. The animal‐inspired EAs herein identified concern algorithms that mimic biologic traits of animal (wild) species. Among the animal‐inspired EAs ant colony optimization (ACO), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA), artificial bee colony (ABC), honey bee mating optimization (HBMO), firefly algorithm (FA), cuckoo search (CS) and the bat algorithm (BA) are the best‐known ones selected for this review. This paper presents a brief description of the algorithmic characteristics and various employed improved versions or varieties thereof of each of the stated EAs. Furthermore, the differences between the proposed animal‐inspired EAs and their improved versions are identified by comparing the performance of the implemented animal‐inspired EAs in the reviewed literature. PSO and its varieties have the largest number of reported applications. Our comparison results revealed that constrained, discrete and randomized varieties of the animal‐inspired EAs outperformed unconstrained, continuous and deterministic varieties, respectively because of larger feasible search space, better solution quality and shorter computational time. Moreover, all the animal‐inspired EAs outperformed traditional methods of reservoir optimization, such as nonlinear programming (NLP) and dynamic programming (DP).

Hydrodynamic modeling is a powerful tool to gain understanding of river conditions. However, as widely known, models vary in terms of how they respond to changes and uncertainty in their input parameters. A hydrodynamic river model (MIKE HYDRO River) was developed and calibrated for a flood-prone tidal river located in South East Queensland, Australia. The model was calibrated using Manning's roughness coefficient for the normal dry and flood periods. The model performance was assessed by comparing observed and simulated water level, and estimating performance indices. Results indicated a satisfactory agreement between the observed and simulated results. The hydrodynamic modelling results revealed that the calibrated Manning's roughness coefficient ranged between 0.011-0.013. The impacts of tidal variation at the river mouth and the river discharge from upstream are the major driving force for the hydrodynamic process. To investigate the impacts of the boundary conditions, a new sensitivity analysis approach, based on adding stochastic terms (random noise) to the time series of boundary conditions, was conducted. The main purpose of such new sensitivity analysis was to impose changes in magnitude and time of boundary conditions randomly, which is more similar to the real and natural water level variations compared to impose constant changes of water level. In this new approach, the possible number of variations in simulated results was separately evaluated for both downstream and upstream boundaries under 5%, 10%, and 15% perturbation. The sensitivity analysis results revealed that in the river under study, the middle parts of the river were shown to be more sensitive to downstream