A novel chaotic dragonfly algorithm based on sine-cosine mechanism for optimization design

Peng, Junhang; Ye, Yin; Chen, Sixiao; Dong, Chen

doi:10.1109/iciscae48440.2019.221614

Cited by 5 publications

(9 citation statements)

References 15 publications

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“…A chaotic DA based on sine-cosine mechanism (SC-DA) is proposed in [15]. The algorithm is examined using numerical benchmark functions which are continuous and single-objective, and hence a continuous and single-objective version of the algorithm is proposed.…”

Section: Hybrids Of Da Which Handle Continuous and Single-objective Problemsmentioning

confidence: 99%

“…The MHDA algorithm [8], modified DA using Brownian motion [10], hybrid DADE [11], Coulomb force search strategy-based DA [12], SC-DA [15] and QGDA [16], which have been discussed in the earlier sections, have also been applied to multi-objective problems, and continuous and multi-objective versions of these algorithms have also been proposed. The continuous and multi-objective version of the MHDA algorithm [8], modified DA using Brownian motion [10], hybrid DADE [11] and QGDA [16] have been used for the optimal design of a welded beam.…”

Section: Hybrids Of Da Which Handle Continuous and Multi-objective Problemsmentioning

confidence: 99%

“…The continuous and multi-objective version of the MHDA algorithm [8], modified DA using Brownian motion [10], hybrid DADE [11] and QGDA [16] have been used for the optimal design of a welded beam. The Coulomb force search strategy-based DA [12] has been applied for the optimal design of a bucket wheel reclaimer (BWR), and the SC-DA [15] has been applied for the optimal design of a cantilever beam.…”

Section: Hybrids Of Da Which Handle Continuous and Multi-objective Problemsmentioning

confidence: 99%

“…The SC-DA algorithm [15] and the hybrid DA-modified conjugate gradient method [18] improve the effectiveness of the original DA by improving its initialization stage. However, since improving the effectiveness is their only objective, their efficiency is not considered or compared to the efficiency of the original DA.…”

Section: Hybrids Of Da Which Improve Its Effectiveness By Improving Initializationmentioning

confidence: 99%

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

Emambocus

Jasser

Amphawan

2021

Sensors

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Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

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Section: Hybrids Of Da Which Handle Continuous and Single-objective Problemsmentioning

confidence: 99%

Section: Hybrids Of Da Which Handle Continuous and Multi-objective Problemsmentioning

confidence: 99%

Section: Hybrids Of Da Which Handle Continuous and Multi-objective Problemsmentioning

confidence: 99%

Section: Hybrids Of Da Which Improve Its Effectiveness By Improving Initializationmentioning

confidence: 99%

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

Emambocus

Jasser

Amphawan

2021

Sensors

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show abstract

“…In order to obtain better performance, a series of DA variant algorithms are proposed. Peng et al introduced a novel chaotic dragonfly algorithm based on sine-cosine mechanism (SC-DA) for optimization design [43], Song et al also proposed an elite opposition learning and exponential function steps-based dragonfly algorithm for global optimization [44]. And through numerical benchmark functions, the excellent performance of the two methods is verified.…”

Section: Introductionmentioning

confidence: 99%

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

Dai

Cui

et al. 2020

IEEE Access

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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.

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Chaotic active swarm motion in jellyfish search optimizer

Rajpurohit

Sharma

2022

Int J Syst Assur Eng Manag

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A novel chaotic dragonfly algorithm based on sine-cosine mechanism for optimization design

Cited by 5 publications

References 15 publications

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

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

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

Chaotic active swarm motion in jellyfish search optimizer

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