Ensemble mutable smart bee algorithm and a robust neural identifier for optimal design of a large scale power system

•Proposing a novel ensemble bio-inspired method for optimizing a real-world engineering problem.•Proposing an automated mechanism for designing a robust neural network identifier.•Elaborating on the authenticity of the proposed framework.•Modeling a large-scale power system to check the validity of the proposed method in practice.

The aim of the current study is to probe the potentials of ensemble bio-inspired approaches to handle the deficiencies associated with designing large scale power systems. Ensemble computing has been proven to be a very promising paradigm. The fundamental motivation behind designing such bio-inspired optimization models lies in the fact that interactions among different sole optimizers can afford much better income as compared with an individual optimizer. To do so, the authors propose an optimization technique called ensemble mutable smart bee algorithm (E-MSBA) which is based on the aggregation of several independent low-level optimizers. Here, each low-level unit of the proposed ensemble framework uses mutable smart bee algorithm (MSBA) for optimization procedure. The main provocations behind selecting MSBAs of different properties as components of ensemble are twofold. On the one hand, MSBA proved its capability for handling multimodal constraint problems. On the other hand, based on different experiments, it was demonstrated that MSBA can find the optimum solution with a relatively low computational cost. In this study, the authors intend to indicate that the proposed ensemble paradigm can efficiently optimize the operating parameters of a large scale power system which includes different mechanical components. To this end, E-MSBA and some rival methods are taken into account for the optimization procedure. The obtained results reveal that E-MSBA inherits some positive features of the MSBA algorithm. Additionally, it is observed that the ensembling approach enables the proposed method to effectively tackle the flaws associated with optimization of large scale problems.