This manuscript introduces a new socio-inspired metaheuristic technique referred to as Leader-Advocate-Believer based optimization algorithm (LAB) for engineering and global optimization problems. The proposed algorithm is inspired by the AI-based competitive behaviour exhibited by the individuals in a group while simultaneously improving themselves and establishing a role (Leader, Advocate, Believer). LAB performance in computational time and function evaluations are benchmarked using other metaheuristic algorithms. Besides benchmark problems, the LAB algorithm was applied for solving challenging engineering problems, including abrasive water jet machining, electric discharge machining, micro-machining processes, and process parameter optimization for turning titanium alloy in a minimum quantity lubrication environment. The results were superior to the other algorithms compared such as Firefly Algorithm, Variations of Co-hort Intelligence, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimisation, and Multi-Cohort Intelligence. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features of the LAB algorithm along with its limitations are also discussed.

翻译：本手稿引入了一种新的由社会启发的计量经济学新技术,称为 " 首席水动喷气式机械化 " (LAB),用于工程和全球优化问题;提议的算法受一个群体的个人在自我改进和确立角色(Leader、倡导者、信仰者)的同时展示的基于AI的竞争性行为启发;计算时间和功能评估的绩效使用其他计量经济学基准;除了基准问题外,LAB算法还用于解决具有挑战性的工程问题,包括研磨式水喷气机机械化、电放电机械化、微处理工艺和在最低润滑环境下将钛合金转成铝的流程参数优化;其结果优于其他算法,如Firefly Algorithm、Conyal Algorithm、模拟Annaaling、Particle Swarm Opimization和多Cohort Int Intrience等;该研究的结果突出显示,LAB在功能和计算时的功能限制方面优于其他算法特征。