Modified dragonfly algorithm based multilevel thresholding method for color images segmentation

Xiao, Peng; Jia, He; Lang, Chun Bo

doi:10.3934/mbe.2019324

Cited by 7 publications

(8 citation statements)

References 57 publications

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“…The DA with opposition-based learning (OBLDA) [13], hybrid DA-DE algorithm with chaotic maps and elite opposition-based learning [17] and hybrid DA-opposition-based learning [23], which have been discussed earlier, also improve the effectiveness of the original DA by means of improving its initialization stage. However, these hybrids of DA do not increase its efficiency.…”

Section: Gradient Methods Initializationmentioning

confidence: 99%

“…A hybrid DA-DE algorithm with chaotic maps and elite opposition-based learning (EOBL) is proposed in [17]. The algorithm is used in multilevel thresholding image segmentation to obtain the optimal threshold values.…”

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

confidence: 99%

“…The EOBL [17] algorithm, hybrid improved DA [28] and DA-GA [32], improve both the exploitation and exploration phases of DA, thereby increasing its effectiveness. However, the efficiency of these algorithms is lower than that of the original DA.…”

Section: Hybrids Of Da Which Improve Its Effectiveness By Improving Both Exploitation and Explorationmentioning

confidence: 99%

“…The hybrid DA-DE algorithm with chaotic maps and elite opposition-based learning (EOBL) [17] improves the effectiveness of DA by better balancing the exploration and exploitation phases, and by enhancing the initialization of the population. Chaotic maps and EOBL are first used to provide fitter initial positions for the dragonflies by increasing the randomness of the population.…”

Section: Hybrids Of Da Which Improve Its Effectiveness By Improving Both Exploitation and 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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Section: Gradient Methods Initializationmentioning

confidence: 99%

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

confidence: 99%

Section: Hybrids Of Da Which Improve Its Effectiveness By Improving Both Exploitation and Explorationmentioning

confidence: 99%

Section: Hybrids Of Da Which Improve Its Effectiveness By Improving Both Exploitation and 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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“…To deal with the problems in the real-time path planning for the hybrid UAV/UGV system, an improve bio-inspired methods is proposed, which is combined with the Dragonfly Algorithm (DA) [28], [29] and the bio-inspired neural network (BINN) [23], [30]. In the proposed method, the motion space of the hybrid UGV/UAV system is converted into a topological state space composed of multiple neurons.…”

Section: Introductionmentioning

confidence: 99%

An Improved Real-Time Path Planning Method Based on Dragonfly Algorithm for Heterogeneous Multi-Robot System

Wang

Tang

et al. 2020

IEEE Access

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Heterogeneous multi-robot system is one of the most important research directions in the robotic field. Real-time path planning for heterogeneous multi-robot system under unknown 3D environment is a new challenging research and a hot spot in this field. In this paper, an improved real-time path planning method is proposed for a heterogeneous multi-robot system, which is composed of many unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). In the proposed method, the 3D environment is modelled as a neuron topology map, based on the grid method combined with the bio-inspired neural network. Then a new 3D dynamic movement model for multi-robots is established based on an improved Dragonfly Algorithm (DA). Thus, the movements of the robots are optimized according to the activities of the neurons in the bio-inspired neural network to realize the real-time path planning. Furthermore, some simulations have been carried out. The results show that the proposed method can effectively guide the heterogeneous UAV/UGV system to the target, and has better performance than traditional methods in the real-time path planning tasks.

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Dragonfly algorithm: a comprehensive review and applications

Meraihi

Ramdane-Chérif

Acheli

et al. 2020

Neural Comput & Applic

119

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Modified dragonfly algorithm based multilevel thresholding method for color images segmentation

Cited by 7 publications

References 57 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 Real-Time Path Planning Method Based on Dragonfly Algorithm for Heterogeneous Multi-Robot System

Dragonfly algorithm: a comprehensive review and applications

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