Hunger games pattern search with elite opposite-based solution for solving complex engineering design problems

Abstract

The hunger games search (HGS) algorithm is designed to tackle optimization problems, however, issues such as local minimum stagnation and immature convergence hinder its effectiveness. To address these limitations, this study introduces a novel improved HGS (Imp-HGS) algorithm. The Imp-HGS algorithm uses pattern search (PS) and elite opposition-based learning (OBL) mechanisms to enhance exploitation and exploration, respectively. The algorithm's performance is tested across the CEC2019 and CECE2020 test suites along with three different engineering design problems, including identifying an infinite impulse response (IIR) model, training a multilayer perceptron (MLP), and designing a proportional-integral-derivative (PID) controller for a doubly fed induction generator (DFIG)-based wind turbine system. The test functions demonstrated superior performance of the Imp-HGS algorithm over a wide range of state-of-the-art algorithms. The ablation tests using CEC2020 test suite also demonstrate the wisely integration of the PS and elite OBL mechanisms as significant improvements are achieved. The statistical results demonstrate the significance of Imp-HGS in the IIR system identification as it consistently achieved lower average errors, lower standard deviations, competitive best results, and satisfactory worst results compared to the other algorithms. Moreover, the Imp-HGS algorithm consistently demonstrates better performance in terms of average classification rates across various datasets, showcasing its effectiveness in solving classification problems, making it a good tool for MLP training. Lastly, the Imp-HGS algorithm's ability to eliminate overshoot, achieve faster rise time, shorter settling time, and minimal peak time showcases its effectiveness in achieving stable and efficient operation of the wind turbine system. The computational times also confirm the efficacy of the Imp-HGS algorithm for all considered real-world engineering problems. Overall, the results show that the proposed algorithm outperformed other competitive approaches, cementing its status as a highly promising tool for tackling a wide range of complex engineering optimization problems.