Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs

Abstract

The honey-bee mating process is considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating optimization (HBMO) algorithm is applied to extract the linear monthly operation rules of reservoirs for both irrigation and hydropower purposes. The release rules for each month are considered as a linear function of the reservoir past-month-end storage as well as current monthly inflow to the reservoir. In such a case, the decision variables are 36 for each problem and are set so that water supply deficits are minimized. In both irrigation and hydropower purposes, 60–480 months are considered and results are compared to those from the nonlinear programming solver of the LINGO 8.0 software. The approach and the rules tend to be very promising and denote the capability of the proposed HBMO algorithm in solving reservoir operation problems. Furthermore, the results indicated that, by using the near-optimal solution from the HBMO as a starting point for the NLP solver, the obtained objective function value was enhanced significantly and a better local optimum was found.