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An Adaptive Whale Optimization Algorithm with Mahalanobis Distance for Optimization Problems
Author(s)
Date Issued
January 1, 2022
Type
Conference Paper
Abstract
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.
Citation
7th International Conference on Digital Arts Media and Technology Damt 2022 and 5th Ecti Northern Section Conference on Electrical Electronics Computer and Telecommunications Engineering Ncon 2022, 285-289, 2022