Hybrid Particle Swarm Optimization with a Modified Arithmetical Crossover for Solving Unconstrained Optimization Problems

2015 ◽  
Vol 53 (3) ◽  
pp. 125-141
Author(s):  
Ahmed Ali ◽  
Mohamed Tawhid
Keyword(s):  
Particle Swarm Optimization ◽  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Unconstrained Optimization Problems

scholarly journals A Hybrid Particle Swarm Optimization-Cuckoo Search Algorithm and Its Engineering Applications

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Jinjin Ding ◽  
Qunjin Wang ◽  
Qian Zhang ◽  
Qiubo Ye ◽  
Yuan Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Hammerstein Model ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

This paper deals with the hybrid particle swarm optimization-Cuckoo Search (PSO-CS) algorithm which is capable of solving complicated nonlinear optimization problems. It combines the iterative scheme of the particle swarm optimization (PSO) algorithm and the searching strategy of the Cuckoo Search (CS) algorithm. Details of the PSO-CS algorithm are introduced; furthermore its effectiveness is validated by several mathematical test functions. It is shown that Lévy flight significantly influences the algorithm’s convergence process. In the second part of this paper, the proposed PSO-CS algorithm is applied to two different engineering problems. The first application is nonlinear parameter identification for the motor drive servo system. As a result, a precise nonlinear Hammerstein model is obtained. The second one is reactive power optimization for power systems, where the total loss of the researched IEEE 14-bus system is minimized using PSO-CS approach. Simulation and experimental results demonstrate that the hybrid optimal algorithm is capable of handling nonlinear optimization problems with multiconstraints and local optimal with better performance than PSO and CS algorithms.

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