Enhanced crow search algorithm for AVR optimization

Abstract

This paper proposes an enhanced crow search algorithm (ECSA) for solving numerical and real-life engineering problems. Novelties of the proposed method are fourfold: (1) addition of an archive component in the standard crow search algorithm (CSA) to incorporate past experience of finding solution (2) formulation of non-hideout position so that crow will remain near its hideout position, (3) Rechenberg’s 1/5th rule is exploited to change the flight length (instead of fixed) to speed up optimization process and (4) awareness probability is regulated to set a trade-off between local and global exploration. The performance of proposed technique is investigated on 23 benchmark functions such as unimodal, multimodal and fixed-dimension multimodal benchmark functions. The results of ECSA are compared to other state-of-the-art metaheuristic algorithms, in which ECSA outperformed other algorithms in majority of the benchmark functions. Further, to validate the effectiveness of the proposed method, ECSA has been used for optimization of proportional–integral–derivative (PID) controller. Results of ECSA–PID have been compared with conventional CSA as well as with other state-of-the-art techniques like Ziegler–Nichols (Z–N), Kitamori, ACO, multi-objective ACO, multi-objective GA and fuzzy and space gravitational optimization algorithm. The proposed algorithm is implemented on the AVR system and tested under various conditions for robustness. Consistency in the results on benchmark systems as well as on their variants and AVR system and its variants prove the robustness of the proposed method. Also, the performance of the proposed algorithm is found to be better than the existing techniques.