Abstract
This study implements a novel meta-heuristic method called artificial gorilla troop optimization (AGTO) to handle the short-term fixed-head hydrothermal scheduling problem (ST-HTS). In the study, the constraints of reservoir and the fuel function of total generating electricity cost for thermal power plants are taken into account. AGTO is a newly published method and the main inspiration is based on the living practice of gorilla in nature. While tested by the hydrothermal power system, AGTO have demonstrated its striking performance to other state-of-the-art meta-heuristic algorithms and other popular algorithms. In this study, AGTO is implemented alongside with improved particle swarm optimization (IPSO) and tunicate swarm algorithm (TSA) for assessing the raw performance. The results obtained by these methods see that AGTO is a highly effective computing method for an engineering problem such ST-HTS besides IPSO and TSA in all compared criteria.
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