Abstract
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.
Highlights
Optimization problems are ubiquitous and critical in controller design [1], system identification [2], power systems [3], etc
The particle swarm optimization-Cuckoo Search (PSO-Cuckoo Search (CS)) is performed several times with the final results listed in Table 8, and the calculation results of parameter estimation using particle swarm optimization (PSO) and CS are shown
In the traditional PSO, original CS, and the hybrid PSO-imperialist competitive algorithm (PSO-ICA), the values are 0.1947, 0.1876, and 0.1864, which are higher than our proposed method
Summary
Optimization problems are ubiquitous and critical in controller design [1], system identification [2], power systems [3], etc. These engineering problems are often nonlinear with various variables under complex constraints. Modern metaheuristic algorithms have been developed with an aim to carry out global search. Two important characteristics of the metaheuristic algorithms are intensification and diversification. Intensification focuses around the current best solutions and selects the best candidates or solutions, while diversification makes sure that the algorithm can explore the search space more efficiently, often by randomization [6]
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