Feature Selection for Metaheuristics Optimization Technique with Chaos

Chaudhury S., Oyebode O. J., Ngo Hoang D., Rabbi F., AJIBADE S. M.

18th IEEE International Colloquium on Signal Processing and Applications, CSPA 2022, Selangor, Malezya, 12 Mayıs 2022, ss.365-370 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/cspa55076.2022.9781989
  • Basıldığı Şehir: Selangor
  • Basıldığı Ülke: Malezya
  • Sayfa Sayıları: ss.365-370
  • Anahtar Kelimeler: Particle swarm optimization, Feature selection, Differential evolution, Chaotic map
  • İstanbul Ticaret Üniversitesi Adresli: Hayır

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.