TY - JOUR
T1 - An improved harris hawks optimization algorithm with simulated annealing for feature selection in the medical field
AU - Elgamal, Zenab Mohamed
AU - Yasin, Norizan Binti Mohd
AU - Tubishat, Mohammad
AU - Alswaitti, Mohammed
AU - Mirjalili, Seyedali
N1 - Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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 (P-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.
AB - 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 (P-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.
KW - Chaos theory
KW - Feature selection
KW - Harris Hawks optimization (HHO) algorithm
KW - Simulated annealing (SA)
KW - Wrapper method
UR - http://www.scopus.com/inward/record.url?scp=85102799308&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3029728
DO - 10.1109/ACCESS.2020.3029728
M3 - Article
AN - SCOPUS:85102799308
VL - 8
SP - 186638
EP - 186652
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -