Multi-cohort whale optimization with search space tightening for engineering optimization problems

Metaheuristic algorithms have been widely studied and shown to be suitable for solving various engineering optimization problems. This paper presents a novel variant of the whale optimization algorithm known as multi-cohort whale optimization algorithm to solve engineering optimization problems. The new algorithm improves the existing whale optimization by dividing the population in to cohorts and introduces a separate exploration procedure for each cohort. Also, a new boundary update procedure for the search space is introduced. In addition to this, opposition-based initialization and elitism are employed to aid quick convergence of the algorithm. The proposed algorithm is compared with whale optimization algorithm variants and other metaheuristic algorithms for different numerical optimization problems. Statistical analysis is performed to ensure the significance of the proposed algorithm. In addition to this, the proposed and existing algorithms are studied based on three engineering optimization problems. The analyses show that the proposed algorithm achieves 53.75% improvement in average fitness when compared to the original whale optimization algorithm.