Research on the Improved Dragonfly Algorithm-Based Flexible Flow-Shop Scheduling

Han, Zhonghua; Zhang, Jingyuan; Shao, Lin

doi:10.1007/978-981-15-0474-7_20

Cited by 4 publications

(8 citation statements)

References 14 publications

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“…In [22], a hybrid DA-Simulated Annealing algorithm is proposed. The algorithm is applied to the flexible flow-shop scheduling to optimize the online scheduling sequence.…”

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

confidence: 99%

“…Steps to be improved: Lines The hybrid DA-opposition-based learning [23] and hybrid DA-simulated annealing algorithms [22] successfully increase the effectiveness of DA by means of improving its exploration. However, they are not able to increase the efficiency of DA.…”

Section: Hyper Learning Strategy Explorationmentioning

confidence: 99%

“…However, they are not able to increase the efficiency of DA. The hybrid DAopposition-based learning [23] has the same efficiency as the original DA, while hybrid DA-simulated annealing algorithm [22] has lower efficiency. An explanation of these algorithms which is not given in Table 3 is presented next.…”

Section: Hyper Learning Strategy Explorationmentioning

confidence: 99%

“…The hybrid DA-simulated annealing algorithm in [22] enhances the effectiveness of DA by improving its ability to escape the local optimum. It makes use of the selection probability of simulated annealing to retain new individuals.…”

Section: Hyper Learning Strategy Explorationmentioning

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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“…In [22], a hybrid DA-Simulated Annealing algorithm is proposed. The algorithm is applied to the flexible flow-shop scheduling to optimize the online scheduling sequence.…”

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

confidence: 99%

Section: Hyper Learning Strategy Explorationmentioning

confidence: 99%

Section: Hyper Learning Strategy Explorationmentioning

confidence: 99%

Section: Hyper Learning Strategy Explorationmentioning

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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“…17 Han et al proposed a hybrid method (IDA) based on the hybrid of DA and SA to solve the problem of flexible flow shop scheduling with limited buffer zone. 18 Mahseur et al propose a new meta-heuristic algorithm of dragonfly algorithm and quantum evolution algorithm to solve the problem of quality of service multicast routing. 19 Xu et al proposed an improved DA method based on a mixture of DA and DE for multi-level color image segmentation.…”

Section: Related Work With Da Hybrid Algorithmmentioning

confidence: 99%

Wind driven dragonfly algorithm for global optimization

Zhong

Zhou

Luo

et al. 2020

Concurrency and Computation

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Dragonfly algorithm (DA) is a new swarm intelligence optimization algorithm based on the static and dynamic swarm behavior of dragonflies. The algorithm has the characteristics of simple structure, strong search ability, easy implementation, and strong robustness. However, the DA algorithm itself also has insufficient solution accuracy and slow convergence speed. The Wind Driven Optimization algorithm (WDO) has the characteristics of fast convergence speed and strong global search capability. So as to improve the optimization performance of the DA algorithm and avoid premature convergence, the speed of the WDO is introduced into the later calculation of the algorithm iteration, which speeds up the convergence speed of the global optimal solution. This paper proposes a dragonfly algorithm based on wind driven (WDDA), that is to reduce the blindness of the dragonfly algorithm search, improve the solution accuracy and convergence speed, to improve the overall optimization performance of the algorithm. The 23 benchmark test functions and one engineering example for optimization and comparison experiments. The experimental results show that WDDA algorithm has better performance in function optimization.

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