To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). Due to trade-offs between water supply and flood management, the HHO and OBL-HHO models have configurable thresholds to optimise the KGD reservoir operation. To determine the efficacy of the HHO and OBL-HHO in reservoir optimisation, reliability, vulnerability, and resilience are risk measures evaluated. If inflow categories are omitted, the OBL-HHO meets 71.49% of demand compared to 54.83% for the standalone HHO. The HHO proved superior to OBL-HHO in satisfying demand during medium inflows, achieving 38.60% compared to 20.61%, even though the HHO may have experienced water loss at the end of the storage level. The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. For resilience, the ABC scored 0.16, PSO scored 0.15, and real coded GA scored 0.14 whilst the binary-GA has the worst failure recovery algorithm with 0.09.
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