A hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation

Abstract

The continuous growing demand for water, prolonged periods of drought, and climatic uncertainties attributed mainly to climate change mean surface water reservoirs more than ever need to be managed efficiently. Several optimization algorithms have been developed to optimize multi-reservoir systems operation, mostly during severe dry/wet seasons, to mitigate extreme-events consequences. Yet, convergence speed, presence of local optimums, and calculation-cost efficiency are challenging while looking for the global optimum. In this paper, the problem of finding an efficient optimal operation policy in multi-reservoir systems is discussed. The complexity of the long-term operating rules and the reservoirs' upstream and downstream joint-demands projected in recursive constraints make this problem formidable. The original Coral Reefs Optimization (CRO) algorithm, which is a meta-heuristic evolutionary algorithm, and two modified versions have been used to solve this problem. Proposed modifications reduce the calculation cost by narrowing the search space called a constrained-CCRO and adjusting reproduction operators with a reinforcement learning approach, namely the Q-Learning method (i.e., the CCRO-QL algorithm). The modified versions search for the optimum solution in the feasible region instead of the entire problem domain. The models' performance has been evaluated by solving five mathematical benchmark problems and a well-known continuous four-reservoir system (CFr) problem. Obtained results have been compared with those in the literature and the global optimum, which Linear Programming (LP) achieves. The CCRO-QL is shown to be very calculation-cost-effective in locating the global optimum or near-optimal solutions and efficient in terms of convergence, accuracy, and robustness.

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

“…Surface water reservoirs must be managed more efficiently than ever before due to climatic uncertainties, which may result in increased water demand and prolonged drought if reservoir policy is not adequately publicised and planned for. The CRO and its enhancement techniques were tested in cascading reservoirs using constrained (CCRO) to narrow the search space and reinforcement learning (CCRO-QL) based on machine learning [ 26 ]. Despite the fact that the results showed that the enhancement technique was highly successful in producing optimal reservoir policy, it is also known that the CRO is a new MHA in this field.…”

“…Nevertheless, a variety of reservoir models that were focused on the optimum reservoir operating policy, in order to obtain the maximum benefits of the reservoir system/best trade-off in reservoir policy, can be summarised from the above-mentioned research works conducted over the last decades. Another popular topic that has only recently been discussed is the incorporation of the hedging policy into hydropower reservoir systems through the use of MHAs [ 25 , 26 ]. Concurrently, simulation or modelling (NN, SVM, ANFIS, etc.)…”

Effective Optimization Based on Equilibrium Optimizer for Dynamic Cutting Force Coefficients of the End-Milling Process

“…In [47], a quadratic interpolation approach was introduced with a whale optimization algorithm to solve highdimensional global optimization problems. A machine learning technique was combined with a constrained coral reef optimization algorithm to tune multi-reservoir processing [48]. In [49], a local escaping operator and orthogonal learning were utilized to improve the Archimedes optimization algorithm for PEM fuel cell parameter tuning.…”

Bagging-based Adaptive Synthetic Oversampling Algorithm for Robust Policy Search in Reservoir Management

“…Thereby, efficient policy searching approach to balance the multi-sectoral tradeoffs associated with reservoir policy (e.g., flood control, water supply, and hydropower production) has been the focus of much research (Salazar et al, 2017). Of relevance, heuristic optimization algorithms have received increasing attention (Ahmad et al, 2014;Wang et al, 2021a), including various recently proposed algorithms (Bashiri-Atrabi et al, 2015;Chong et al, 2021;Emami et al, 2021;Rani et al, 2020). With the rapid improvement of computational intelligence in recent decades, heuristic algorithms provide fast computation, global optimal solutions, and superior generality to translate observed and projected information into action (Giuliani et al, 2015;Wang et al, 2021b).…”