Feature Selection for Metaheuristics Optimization Technique with Chaos
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
Particle swarm optimization (PSO) is a global optimization method that is extremely effective. PSO's flaws include slow convergence, premature convergence, and getting stuck at local optima. In this paper, the chaos map and dynamic-weight Particle Swarm Optimization (CPSO) are combined with PSO to improve the search process by adjusting the inertia weight of PSO and changing the position update formula in the Chaos dynamic-weight Particle Swarm Optimization (CPSO), resulting in efficient balancing for local and global PSO feature selection processes. Using eight numerical functions, the performance of CPSO was compared to that of two metaheuristic techniques which are the original PSO and Differential Evolution (DE). The results reveal that the CPSO is an efficient feature selection technique that generates good results by balancing the exploration and exploitation search processes.
Açıklama
Anahtar Kelimeler
Chaotic map; Differential evolution; Feature selection; Particle swarm optimization
Kaynak
2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceeding
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
