LAB: A Leader-Advocate-Believer Based Optimization Algorithm

Abstract

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. The LAB algorithm was applied for solving engineering problems, including abrasive water jet machining, electric discharge machining, micromachining processes and turning of titanium alloy in a minimum quantity lubrication environment. The results were compared with other algorithms such as Firefly Algorithm, Cohort 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 and limitations of the LAB algorithm are also discussed.

An Improved Coati Optimization Algorithm Based on Multi-Strategy

“…Due to the short time for the COA algorithm to be proposed, so far, there is not much new progress in the improvement and application of the COA algorithm, and the original algorithm is only quoted for comparison with the newly proposed algorithm, for example, the Kepler optimization algorithm newly proposed by Abdel-Basset et al [9] in 2023 was compared with the performance of the COA algorithm, and the results showed that both algorithms have good competitiveness. A leader-advocate-believer-based optimization algorithm proposed by Reddy et al [10] was also compared with the COA algorithm, and the results showed that the new algorithm performed better in optimization results.…”