A Hybrid Particle Swarm Optimization‐Cuckoo Search Algorithm and Its Engineering Applications

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.

Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

“…Based on the prior research [37], the advanced MOPSO is run multiple times for the selection of parameters. Finally, in our test, the parameters in advanced MOPSO are set in Table 5.…”

Identification and control of the motor‐drive servo turntable with the switched friction model

“…The proposed PSO-CS algorithm is applied to the optimisation of parameter identification of the switched friction model. The parameters that need to be identified can be listed in a vector X f = T^c, T^s, ω s , δ^0, δ^1, δ^2 as the particle in the PSO-CS algorithm [37]. The fitness function of the SOO problem is fitness…”

Optimising models for prediction of tropospheric scintillation on satellite links

“…The CS optimisation algorithm is based on the parasitic and forced reproduction mechanism of some species of cuckoo birds, which lay their eggs in the nests of other birds (often other species), even expelling the eggs from the invaded nest to increase the probability that theirs will be born. If the host bird becomes aware that the egg is not its own, it will either kick the foreign egg out or abandon its nest and establish a new one, elsewhere [15][16][17]. The CS algorithm must comply with the following three basic rules [15]: (i) a global search process (known as diversification or exploration), in which each cuckoo bird will lay only one egg at a time, and choose the nest randomly, (ii) a local search process (known as intensification or exploitation), in which the best nests, i.e.…”

“…In (2), it is important to emphasise that, if p a < r 1 , then H p a − r 1 = 0, and, consequently, x k+1 i = x k i . For the global search, in the CS algorithm is used Lévy flight [15,16] x k+1…”

“…In this sense, in this work, measurements of tropospheric scintillation reported from Spain [14] will be used to obtain new models for the prediction of the scintillation. For the development of the models, existing models will be optimised, more specifically, those mentioned above, using the computational intelligence technique Cuckoo Search (CS) [15][16][17], aiming to the minimisation of root mean square error (RMSE) existing between the estimated values by the optimised models and the measured values.…”