Influencer buddy optimization: Algorithm and its application to electricity load and price forecasting problem

Abstract

Swarm-based algorithms are widely accepted in different fields of engineering as they have proven themselves effective in solving real-world optimization problems. According to the “No-Free-Lunch” theorem, there is no such algorithm that can perform well in all optimization problems. This paper proposes a novel swarm-based optimization algorithm, influencer buddy optimization (IBO), that mimics human behavior in social environments. This algorithm exploits the fact that humans are influenced by a group of individuals rather than a single person. However, every individual can select a buddy from this group based on their preferences. The mathematical representation of this social human behavior is shown in this paper. The algorithm is tested on twenty-one standard benchmark functions, and twenty-four noiseless black-box optimization benchmarking (BBOB) functions to validate its effectiveness over fourteen optimization algorithms. Further, two hybrid models are proposed by combining IBO with an artificial neural network (ANN) and recurrent neural network (RNN) to solve electricity load and price forecasting problems. The results of hybrid models are compared with nine recent and popular optimization algorithms. The simulation result of hybrid models shows that ANN-IBO and RNN-IBO generate the least forecasting error in their respective classes of network architectures.