LAB: a leader–advocate–believer-based optimization algorithm.

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 behavior 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. The algorithm is validated using the CEC 2005 and CEC 2017 benchmark functions. The algorithm was applied to solve engineering problems, including abrasive water jet machining, electric discharge machining, micro-machining processes and turning of titanium alloy in a minimum quantity lubrication environment. LAB algorithm was validated using the Friedman rank test. The results were compared with other algorithms such as FA, CI, GA, SA, PSO, Multi-CI, CMAES, ABC, SADE, CLPSO, BSA, IA, WOA, SHO, AVOA, LSHADE-Cn-EpsiN, FDB-SFS and LSHADE. For real-world problems, LAB outperformed SA, f best and f better by achieving 76%, 85% and 75% minimization of R a , respectively, for micro-milling with 0.7 mm tool diameter. For real-world problems, LAB achieved 81%, 72%, 85% minimization of R a when compared to SA, f best and f better for 1 mm tool diameter. LAB also achieved 24% and 34% minimization of B h and B t as compared to SA for micro-drilling with a tool diameter 0.5 mm. For tool diameters 0.8 mm and 0.9 mm, 16% and 3% minimization of B t , respectively, were achieved as compared to SA. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features and limitations of the algorithm are also discussed. [ABSTRACT FROM AUTHOR]