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...

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Bibliographic Details
Published in:Proceedings (International Confernce on Computational Intelligence and Communication Networks) pp. 460 - 465
Main Authors: Nkwanyana, Thamsanqa Bongani, Wang, Zenghui
Format: Conference Proceeding
Language:English
Published: IEEE 25.09.2020
Subjects:
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
Online Access:Get full text
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Summary: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