Improved Particle Swarm optimization Base on the Combination of Linear Decreasing and Chaotic Inertia Weights

Particle swarm optimization (PSO) is one of the uncomplicated optimization algorithm, but it also has its disadvantages, such as premature convergence, it is difficult to get the globally optimal solution and it easily falls into local extremes. In this paper, a new PSO is proposed by combining two...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings (International Confernce on Computational Intelligence and Communication Networks) s. 460 - 465
Hlavní autoři: Nkwanyana, Thamsanqa Bongani, Wang, Zenghui
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 25.09.2020
Témata:
Chaotic communication
Convergence
convergence and inertia weight
exploration and exploitation
globally optimal
local extreme
Mathematical model
Matlab
Optimization
Particle swarm optimization
Simulation
ISSN:2472-7555
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Particle swarm optimization (PSO) is one of the uncomplicated optimization algorithm, but it also has its disadvantages, such as premature convergence, it is difficult to get the globally optimal solution and it easily falls into local extremes. In this paper, a new PSO is proposed by combining two types of inertia weights. In order to find a solution for above mentioned disadvantages, the linear decreasing inertia weight is combined with the chaotic inertia weight. The control factor is introduced as an exponential function. The following benchmark functions: Ackley Function, Rastrigin Function, Schwefel Function, Cigar function, Sphere Function, and Booth Function are being used to validate the effectiveness of the improved PSO and the simulation results show that the proposed PSO can achieve promising performance.
ISSN:2472-7555
DOI:10.1109/CICN49253.2020.9242603