Hbmo in Optimal Reservoir Operation

The broad of applicability, ease of use, and global perspective of so-called meta-heuristic algorithms may be considered as the primary reason for their extensive application and success as search and optimization tools in various problem domains. Honey bees are among the most well-studied social insects. Their mating process may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey bees mating. This paper presents a honey bee mating optimization algorithm (HBMO) to solve a real world continuous optimization problem. To do so, a single reservoir with 60 operation period is considered. A fitness function is defined as the total square deviation from target demand. Releases from the reservoir are considered as decision variables, resulting in reservoir storage as continuous state variable. Employing one queen with 130 drones and 1000 mating flights, the model converged to a near global optimum. Results obtained from 10 different runs are quite promising emphasizing the capability of the developed algorithm in handling constrained-continuous engineering optimization problems.