Open Access
ARTICLE
An Improved Whale Optimization Algorithm for Feature Selection
Wenyan Guo1, *, Ting Liu1, Fang Dai1, Peng Xu1
1 School of Science, Xi’an University of Technology, Xi’an, 710054, China.
* Corresponding Author: Wenyan Guo. Email: .
Computers, Materials & Continua 2020, 62(1), 337-354. https://doi.org/10.32604/cmc.2020.06411
Abstract
Whale optimization algorithm (WOA) is a new population-based metaheuristic
algorithm. WOA uses shrinking encircling mechanism, spiral rise, and random
learning strategies to update whale’s positions. WOA has merit in terms of simple
calculation and high computational accuracy, but its convergence speed is slow and it is
easy to fall into the local optimal solution. In order to overcome the shortcomings, this
paper integrates adaptive neighborhood and hybrid mutation strategies into whale
optimization algorithms, designs the average distance from itself to other whales as an
adaptive neighborhood radius, and chooses to learn from the optimal solution in the
neighborhood instead of random learning strategies. The hybrid mutation strategy is used
to enhance the ability of algorithm to jump out of the local optimal solution. A new whale
optimization algorithm (HMNWOA) is proposed. The proposed algorithm inherits the
global search capability of the original algorithm, enhances the exploitation ability,
improves the quality of the population, and thus improves the convergence speed of the
algorithm. A feature selection algorithm based on binary HMNWOA is proposed. Twelve
standard datasets from UCI repository test the validity of the proposed algorithm for
feature selection. The experimental results show that HMNWOA is very competitive
compared to the other six popular feature selection methods in improving the
classification accuracy and reducing the number of features, and ensures that HMNWOA
has strong search ability in the search feature space.
Keywords
Cite This Article
W. Guo, T. Liu, F. Dai and P. Xu, "An improved whale optimization algorithm for feature selection,"
Computers, Materials & Continua, vol. 62, no.1, pp. 337–354, 2020. https://doi.org/10.32604/cmc.2020.06411
Citations