An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima...

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Bibliographic Details
Published in:IEEE access Vol. 8; pp. 186638 - 186652
Main Authors: Elgamal, Zenab Mohamed, Yasin, Norizan Binti Mohd, Tubishat, Mohammad, Alswaitti, Mohammed, Mirjalili, Seyedali
Format: Journal Article
Language:English
Published: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Chaos
chaos theory
Classification algorithms
Datasets
Feature extraction
Feature selection
Genetic algorithms
Harris Hawks optimization~(HHO) algorithm
Heuristic methods
Machine learning
Machine learning algorithms
Optimization
Optimization algorithms
Particle swarm optimization
Simulated annealing
simulated annealing (SA)
Sociology
Statistical tests
Statistics
wrapper method
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Summary:Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon's statistical test (<inline-formula> <tex-math notation="LaTeX">P </tex-math></inline-formula>-value), and convergence curve. Based on the achieved results, CHHO confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3029728