An Improved Dragonfly Algorithm With Higher Exploitation Capability to Optimize the Design of Hybrid Power Active Filter

Abstract

Hybrid power active filter (HAPF) is an important device to suppress the harmonics of the power system. In HAPF, the parameters estimation has a great impact on ensuring the power quality in the power system. Aiming at the problem of minimizing the harmonic pollution (HP) in the power system, this paper proposes a new technology namely IEDA for parameter optimization of hybrid power active filters, which is an improved dragonfly algorithm (DA) with higher exploitation capability. DA is a global search algorithm with sufficient ability to avoid falling into local optimization, however, DA performs poorly for local search. In the IEDA, we adopt a strategy of division of labor to divide particles into exploitation population and exploration population. In the exploitation population, we introduce the information exchange mechanism of the differential evolution (DE) and set up an exemplar pool to enhance its exploitation capability. In the exploration population, we use the global search ability of the DA to prevent particles from falling into a local optimum. Through the division of labor between exploitation population and exploration population, the problems of low accuracy and slow convergence of DA are effectively solved. Experimental results show that the algorithm has greatly improved accuracy and reliability compared with seven well-established algorithms.

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

“…In this section, the hybrids of DA which have been applied to continuous and multiobjective problems are discussed. Some of these algorithms, specifically the ones proposed in [29][30][31][32], have only been applied to continuous and multi-objective problems, and hence only the multi-objective version of the algorithms has been proposed.…”

“…In [31], a hybrid DA-DE, called IEDA, is proposed for the optimal design of a hybrid power active filter. Since this is a multi-objective problem where the parameters can have any real value within a specified range, the continuous and multi-objective version of the algorithm is proposed and used.…”

“…The algorithm has an iterative level hybridization where DA is first applied, and then the best solution obtained by DA is further updated using the whale optimization algorithm. There are two equations of the whale optimization algorithm which are added to DA as shown in The hybrid DA-PSO algorithm [29] and IEDA [31] aim at increasing the effectiveness of DA by means of improving its exploitation, and they are successful in achieving their objective. However, the efficiency of the hybrid algorithms is lower than that of the original DA.…”

“…The parameters C 1 and C 2 are acceleration coefficients, rand 1 and rand 2 are random values in range [0,1], w is the inertia weight, and X t+1 DA is the best position obtained by DA. The hybrid DA-DE, called IEDA [31], improves the effectiveness of DA by enhancing its exploitation capability so as to surmount the problem of low accuracy of solutions. A division of labor strategy is employed so as to divide the population into two halves.…”

“…The MHDA algorithm [8], the modified DA using Brownian motion [10] and hybrid DADE [11] have been used for the welded beam design problem, which consists of minimizing the fabrication cost by obtaining an optimal set of structural parameters of the beam. Moreover, the Coulomb force search strategy-based DA [12] has been applied to the bucket wheel reclaimer (BWR) optimization to determine the optimal parameters to optimize the structure of a BWR and to a 25 and a 72-bar space truss structure problem, Chaotic DA based on sine-cosine mechanism (SC-DA) [15] has been used for the cantilever beam design problem to optimize the parameters of the beam, such that its weight is minimized and Hybrid DA-DE (IEDA) [31] has been used for the optimal design of a hybrid power active filter. The quantum-behaved and Gaussian mutational DA (QGDA) [16] has been used for the the welded beam design problem, the multiple disk clutch brake design problem and I-beam design problem.…”