Jitkongchuen, Duangjai, Sirikayon, Chaloemphon and Thammano, Arit (2022) An Adaptive Whale Optimization Algorithm with Mahalanobis Distance for Optimization Problems In: The 7th International Conference on Digital Arts, Media and Technology (DAMT) and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (NCON), 26-28 January 2022, Chiang Rai, Thailand.
Official URL: https://ieeexplore.ieee.org/abstract/document/9720342
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.
Item Type:
Conference or Workshop Item (Paper)
Identification Number (DOI):
Subjects:
Subjects > Computer Science > Artificial Intelligence
Subjects > Computer Science > Machine Learning
Subjects > Computer Science > Neural and Evolutionary Computation
Divisions:
Deposited by:
Arit Thammano
Date Deposited:
2022-02-11 23:52:48
Last Modified:
2022-04-20 08:09:23