Optimization is the core element in any decision-making problem. It has its application in several real-world problems related to resource management, planning, engineering application, economic analysis, etc. A novel optimization algorithm, Elitist Non dominated Sorting directional Bat Algorithm (ENSdBA) has been developed and applied for solving a multi objective constrained problem. This method preserves elitism at every step so that the configuration of the entire population is at its best at every steps, eventually reaching the Pareto optimal front. In the traditional Non dominated Sorting Genetic Algorithm-II (NSGA-II), the solutions of two generations are merged to form an intermediate generation from which the final solution is prepared after comparing the solution of both generations. In the proposed algorithm, at every step, one single solution is generated and compared and checked for acceptance which eventually reduces the computational time. The proposed technique adapts nondomination sorting, which can be applied to any other metaheuristic algorithm. The ENSdBA is applied to some of the widely used benchmark problems. The

results show that the proposed algorithm is highly competitive with other algorithms and outperforms other algorithms in some cases. The benchmark problem for which the algorithm was applied includes: parameter estimation in Muskingum routing, curve matching in confined aquifer, and four reservoir optimization problem. The proposed algorithm is being applied to derive operational policies for multi reservoir system of Krishna basin. Since most of the major rivers in India have their course in more than one state and majority of the reservoirs are operated independently hence leading to interstate conflicts. Even these operations are based on some empirical rules but not on optimized operational policies. A detailed simulation model has been developed to study the complex interstate multi reservoir system of Krishna basin, and to derive the operating policies acceptable stakeholder’s state.