Fuzzy Multilevel Image Thresholding Based on Modified Discrete Grey Wolf Optimizer and Local Information Aggregation

Li, Linguo; Sun, Lijuan; Kang, Wei; Guo, Jian; Han, Chong; Li, Shujing

doi:10.1109/access.2016.2613940

Cited by 58 publications

(30 citation statements)

References 36 publications

(81 reference statements)

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“…Before that, they verified the same test experiment through Harmony Search Optimization and obtained similar results [20]. In our previous work [30], we also take Discrete Grey Wolf Optimizer (GWO) as the tool, with fuzzy theory and fuzzy logic to achieve image segmentation. Compared with EMO and DE, our method shows better performance in segmentation quality and stability.…”

Section: Related Workmentioning

confidence: 66%

Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

Sun

Guo

et al. 2017

Computational Intelligence and Neuroscience

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The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur's entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.

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Section: Related Workmentioning

confidence: 66%

Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

Sun

Guo

et al. 2017

Computational Intelligence and Neuroscience

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“…This paper will compare the proposed algorithm with the best-so-far methods, while the proven inferior ones will be sidelined. In the following sections, the proposed algorithm will be compared to the electro-magnetism method, the standard ABC presented in literature [5], and [23] MDGWO [20], respectively. Electro-magnetism optimization and MDGWO with Kapur's entropy as the objective function are so far the newest intelligent optimizations employed in multilevel image thresholding.…”

Section: Experiments and Results Discussionmentioning

confidence: 99%

“…Their experimental results demonstrate that the PLBA-based thresholding algorithms are much faster than Basic BA, Bacterial Foraging Optimization (BFO), and quantum mechanisms (quantum-inspired algorithms) and perform better than the non-metaheuristic-based two-stage multi-threshold Otsu method (TSMO) in terms of the segmented image quality. In our previous work [20], we take Kapur's entropy as the optimal objective function, with Modified Discrete Grey Wolf Optimizer (MDGWO) as the tool to achieve image segmentation. Compared with EMO, GWO, and DE (Differential Evolution), MDGWO shows better performance in segmentation quality, objective function and stability…”

Section: Related Workmentioning

confidence: 99%

A Quick Artificial Bee Colony Algorithm for Image Thresholding

Sun

Guo

et al. 2017

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Abstract:The computational complexity grows exponentially for multi-level thresholding (MT) with the increase of the number of thresholds. Taking Kapur's entropy as the optimized objective function, the paper puts forward the modified quick artificial bee colony algorithm (MQABC), which employs a new distance strategy for neighborhood searches. The experimental results show that MQABC can search out the optimal thresholds efficiently, precisely, and speedily, and the thresholds are very close to the results examined by exhaustive searches. In comparison to the EMO (Electro-Magnetism optimization), which is based on Kapur's entropy, the classical ABC algorithm, and MDGWO (modified discrete grey wolf optimizer) respectively, the experimental results demonstrate that MQABC has exciting advantages over the latter three in terms of the running time in image thesholding, while maintaining the efficient segmentation quality.

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“…Z max is defined as the max number of iterations. After the initialization, each search agent (i.e., ) have to update its distance from the prey to optimize the candidate solutions in the iterating process [21]. The key steps [20] of the GWO algorithm are presented as follows.…”

Section: The Optimization Objection (P B R Smentioning

confidence: 99%

Martingales-Based ALOHA-Type Grant-Free Access Algorithms for Multi-Channel Networks With mMTC/URLLC Terminals Co-Existence

Chi

Zhao

et al. 2020

IEEE Access

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As a simple single-phase transmission strategy, grant-free access is believed to be an effective way to guarantee the stringent quality of service (QoS) requirements for ultra-reliable low-latency communications (URLLCs). However, unless a theory-based fine evaluation on dynamic delay, we cannot hope to overcome the natural defects of random access and so effectively utilize the time-frequency resources. In this paper, we propose a novel multi-channel ALOHA-type (M-ALOHA) grant-free access algorithm for heterogeneous machine type communication (MTC) networks with URLLC-type terminals and delay-tolerant massive MTC (mMTC)-type terminals coexistence. Firstly, we construct a statistical service model characterizing the transmission rate of each terminal with joint consideration of the features of M-ALOHA access scheme, short packet transmissions and frequency-selective fading channel. Then, taking the great advantages of service-martingales theory in random queuing analysis, we present an ingenious delay analysis and obtain the martingales-based formulation of delay-bound violation probability, where the sporadic feature of MTC traffic arrival is carefully addressed. Finally, the M-ALOHA algorithm is formulated as a system throughput maximization problem subject to martingales-based statistical delay-QoS and the total bandwidth of system. The problem is solved by the proposed bi-objective multi-variable-grey wolf optimizer (BOMV-GWO) algorithm. As a result, we obtain the access probability for each terminal and the optimal parameters for the system design, including the number of sub-channels, the bandwidth for each sub-channel and the packets transmission rate. Simulation results demonstrate that the performance of our M-ALOHA algorithm is favorable. INDEX TERMS Grant-free access, multi-channel, service-martingales theory, ultra-reliable and low-latency communications (URLLCs).

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Fuzzy Multilevel Image Thresholding Based on Modified Discrete Grey Wolf Optimizer and Local Information Aggregation

Cited by 58 publications

References 36 publications

Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

A Quick Artificial Bee Colony Algorithm for Image Thresholding

Martingales-Based ALOHA-Type Grant-Free Access Algorithms for Multi-Channel Networks With mMTC/URLLC Terminals Co-Existence

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