Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency

In this paper, a novel optimization technique based on artificial bee colony algorithm (ABC), which is called as PS-ABCII, is presented. In PS-ABCII, there are three major differences from other ABC-based techniques: (1) the opposition-based learning is applied to the population initialization; (2) the greedy selection mechanism is not adopted; (3) the mode that employed bees become scouts is modified. In order to illustrate the superiority of the proposed modified technique over other ABC-based techniques, ten classical benchmark functions are employed to test. In addition, a hybrid model called PS-ABCII-ELM is also proposed in this paper, which is combined of the PS-ABCII and Extreme Learning Machine (ELM). In PS-ABCII-ELM, the PS-ABCII is applied to tune input weights and biases of ELM in order to improve the generalization performance of ELM. And then it is applied to model and optimize the thermal efficiency of a 300 MW coal-fired boiler. The experimental results show that the proposed model is very convenient, direct and accurate, and it can give a general and suitable way to predict and improve the boiler efficiency of a coal-fired boiler under various operating conditions.