Optimization of an auto drum fashioned brake using the elite opposition-based learning and chaotic k-best gravitational search strategy based grey wolf optimizer algorithm - Yuan, Yongliang; Mu, Xiaokai; Shao, Xiangyu; Ren, Jianji; Zhao, Yong; Wang, Zhenxi (Applied soft computing, 2022)
Highly non-linear optimization problems are widely found in many real-world engineering applications. To tackle these problems, a novel assisted optimization strategy, named elite opposition-based learning and chaotic k-best gravitational search strategy (EOCS), is proposed for the grey wolf optimizer (GWO) algorithm. In the EOCS based grey wolf optimizer (EOCSGWO) algorithm, the elite opposition-based learning strategy (EOBLS) is proposed to take full advantage of better-performing particles for optimization in the next generations. A chaotic k-best gravitational search strategy (CKGSS) is proposed to obtain the adaptive step to improve the global exploratory ability. The performance of the EOCSGWO is verified and compared with those of other seven meta-heuristic optimization algorithms using ten popular benchmark functions. Results show that the EOCSGWO is more competitive in accuracy and robustness, and obtains the first in ranking among the six optimization algorithms. Further, the EOCSGWO is employed to optimize the design of an auto drum fashioned brake. The results show that the braking efficiency factor can be improved by 28.412% compared with the initial design. •Elite opposition-based learning and chaotic k-best gravitational search strategy (EOCS) are proposed.•A novel search strategy is proposed to enhance the global exploratory capability and convergence speed.•The EOCS based grey wolf optimizer (EOCSGWO) algorithm outperforms its peer in terms of searching accuracy and reliability.•EOCSGWO is applied to ensure the parameter of an auto drum fashioned brake.