Abstract
Metaheuristic optimization algorithms have gained relevance and have effectively been investigated for solving complex real design problems in diverse fields of science and engineering. In this paper, a recent meta-heuristic approach inspired by human social concepts, namely the queuing search algorithm (QSA), is implemented for the first time to optimize the main parameters of the spur gear, in particular, to minimize the weight of a single-stage spur gear. The effectiveness of the algorithm introduced is examined in two steps. First, the algorithm used is compared with descriptions in previous studies and indicates that the final results obtained by QSA lead to a reduction in gear weight by 7.5 %. Furthermore, the outcomes obtained are compared with those for the other five algorithms. The results reveal that the QSA outperforms the techniques with which it is compared such as the sine-cosine optimization algorithm, the ant lion optimization algorithm, the interior search algorithm, the teaching-learning-based algorithm, and the jaya algorithm in terms of robustness, success rate, and convergence capability.
Ant lion algorithm for optimized controller gains for power quality enrichment of off-grid wind power harnessing units
