scholarly journals Pattern Recognition Using Particle Swarm Optimization with Proposed a New Conjugate Gradient Parameter in Unconstrained Optimization

2016 ◽  
Vol 19 (3) ◽  
pp. 138-147
Author(s):  
Ban Ahmed Mitras ◽  
◽  
Suhaib Abdul-Jabbar ◽  
Keyword(s):  
Pattern Recognition ◽  
Particle Swarm Optimization ◽  
Unconstrained Optimization ◽  
Conjugate Gradient ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Gradient Parameter

scholarly journals An Enhanced Partial Search to Particle Swarm Optimization for Unconstrained Optimization

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 357 ◽  
Author(s):  
Shu-Kai S. Fan ◽  
Chih-Hung Jen
Keyword(s):  
Particle Swarm Optimization ◽  
Unconstrained Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Searching Strategy ◽  
Wide Range

Particle swarm optimization (PSO) is a population-based optimization technique that has been applied extensively to a wide range of engineering problems. This paper proposes a variation of the original PSO algorithm for unconstrained optimization, dubbed the enhanced partial search particle swarm optimizer (EPS-PSO), using the idea of cooperative multiple swarms in an attempt to improve the convergence and efficiency of the original PSO algorithm. The cooperative searching strategy is particularly devised to prevent the particles from being trapped into the local optimal solutions and tries to locate the global optimal solution efficiently. The effectiveness of the proposed algorithm is verified through the simulation study where the EPS-PSO algorithm is compared to a variety of exiting “cooperative” PSO algorithms in terms of noted benchmark functions.

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