Abstract
The optimal reservoir policy is a complex problem to optimize. This paper introduces the improved teaching learning-based optimization (TLBO) by introducing the elitist concept, in order to improve the convergence, global searchability and faster evolution process. The objective of the present study is to maximize the water allocation for Ukai reservoir, India, to supply water for irrigation, domestic and industrial uses at different dependable inflow. Elitist teaching learning-based optimization (ETLBO) algorithm has been used to optimize water allocation, using four different models having dependable inflow as 60, 65, 70 and 75%. The results from ETLBO are compared with ordinary TLBO, differential evolution (DE), particle swarm optimization (PSO) and linear programming (LP). It was observed that ETLBO performed better in terms of better global searchability and faster convergence than TLBO, DE, PSO and LP.
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