An Adaptive Whale Optimization Algorithm with Mahalanobis Distance for Optimization Problems - Jitkongchuen, Duangjai; Sirikayon, Chaloemphon; Thummano, Arit (My University, 2022)
This paper suggests using Mahalanobis distance to regenerate a new whale position to increase the performance of the whale optimization algorithm. Learning from previous evolutionary searches allows the probability parameters to be self-adapted. The suggested approach was compared to the classical whale optimization algorithm (WOA), particle swarm optimization (PSO), and differential evolution algorithm (DE) on 11 well-known benchmark functions. The results of the experiments showed that the proposed algorithm was effective in solving optimization problems.
Keywords: Benchmark function; Mahalanobis distance; Metaheuristic algorithm; Whale optimization algorithm