Modeling of the Intermediate Point Temperature Using an Elite-Guided Fuzzy Fruit Fly Optimization Algorithm

This paper constructs an improved modeling of intermediate point temperature of the ultra supercritical (USC) power plant, by using an elite-guided fuzzy fruit fly optimization algorithm (EGFFOA). Firstly, to dilute he nonlinearity of the studied system, a network is well constructed, which is connected by fuzzy control strategy and compensation mechanism. Then, to support the network, an elite-guided fruit fly algorithm is adopted to build linear models in local regions. Different from conventional modeling methods, the advantages of the proposed EGFFOA is that local linear models are delicately combined together to achieve the purpose of global modeling by a scheduling algorithm. Finally, the EGFFOA model is compared with the traditional recursive least square method (RLS) and three other meta-heuristics, to show the advantages in approximating the dynamic behavior of USC.