A novel method for image thresholding using interval type-2 fuzzy set and Bat algorithm

Dhar, Soumyadip; Kundu, Malay K.

doi:10.1016/j.asoc.2017.11.032

Cited by 36 publications

(11 citation statements)

References 38 publications

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“…In [18], [19], the fuzzy set is segmented through S or Z membership functions with a membership degree of 0.5. Parameterization greatly limits the universality of the segmentation method based on the fuzzy set [20]. It can be seen that the fuzzy concept can be utilized to explain the intersecting feature interval, but its fuzzy membership function cannot be used to describe the subset of the intersecting feature interval.…”

Section: Image Threshold Segmentation Includes Bi-level Thresholdingmentioning

confidence: 99%

A Defect Detection Method for the Image With Intersecting Feature

Yuan

Liu

2020

IEEE Access

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Since the intersecting feature between the defect and the background of the image, the defect detection often results in under-segmentation or over-segmentation. To solve this problem, we propose a new defect extraction method by calculating the maximum mutual information of intersecting features. Firstly, we construct a new two-dimensional histogram according to the defect features. The new histogram is called Gray Level and Local Spatial Difference histogram (GLSD), which is constructed by grayscale and the improved local gray difference with the spatial relationship. Secondly, considering the geometric distribution of high-probability background events, we improve the segmentation shape of the background event distribution and divide the GLSD histogram preliminary. Finally, we calculate the maximum mutual information of the intersecting feature between the defect and the background. At this point, the boundary of the intersecting feature interval of the GLSD histogram is determined. To verify the effectiveness of the proposed method, we used two sets of databases for performance evaluation. The experimental results show that the proposed method is suitable for non-obvious defect detection under the local uniform background. Meanwhile, it can improve the sensitivity, specificity, and accuracy of defect detection compared with the classical threshold segmentation methods.

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Section: Image Threshold Segmentation Includes Bi-level Thresholdingmentioning

confidence: 99%

A Defect Detection Method for the Image With Intersecting Feature

Yuan

Liu

2020

IEEE Access

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“…The SBA algorithm is derived based on the echolocation mechanism, and it makes bats keep close to the global optimal individual continuously by adjusting the searching frequency [20]. The frequencyfr(i), speed v i and position p i of the ith individual are defined as (20), (21) and (22), respectively [20], [29], [30].…”

Section: A Standard Bat Algorithmmentioning

confidence: 99%

“…The MRFME model is put forward to improve the updated manners of two local searching parameters, which can be expressed as (29) and (30).…”

Section: ) Improvement Of Local Parameter Updating Mannermentioning

confidence: 99%

Applications of Novel Hybrid Bat Algorithm With Constrained Pareto Fuzzy Dominant Rule on Multi-Objective Optimal Power Flow Problems

et al. 2019

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To overcome the premature-convergence of standard bat algorithm in solving the multiobjective optimal power flow (MOOPF) problems, a novel hybrid bat algorithm (NHBA) is proposed in this paper. The suggested NHBA algorithm modifies the local search manner by a monotone random filling model based on extreme (MRFME) and improves the population-diversity by mutation and crossover mechanisms. To obtain the uniformly distributed Pareto optimal set (POS) with zero constraint-violation, an innovative non-dominated sorting method combined with the constrained Pareto fuzzy dominant (CPFD) strategy is put forward in this paper. To verify the superiority of the proposed NHBA-CPFD algorithm, which is federated by the NHBA algorithm and the CPFD strategy, ten MOOPF simulation cases considering the basic fuel cost, the fuel cost with value-point loadings, the total emission, and the active power loss are studied on the IEEE 30-node, IEEE 57-node, and IEEE 118-node systems. In contrast to the NHBA, MOPSO, and NSGA-III algorithms which adopt the constrain-prior Pareto-dominance method (CPM), numerous results validate the NHBA-CPFD algorithm that can achieve more superior compromise solutions and preferable Pareto fronts (PFs) even in the large-scale systems. Furthermore, two performance metrics of generational distance (GD) and hyper-volume (HV) also demonstrate that the NHBA-CPFD algorithm has great advantages to obtain the feasible POS with evenly distribution and favorable-diversity. INDEX TERMS Novel hybrid bat algorithm, multi-objective optimal power flow problem, constrained Pareto fuzzy dominant strategy, monotone random filling model based on extreme, performance metrics.

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“…The bat algorithm (BA), as an optimization method, is based on living bats and the powerful ability of bats to receive sounds from their surroundings. It is used widely in different fields of image processing [27], data sensing systems [28], the determination of the seismic safety of structures [29], the design of wireless sensors [30], and water resource management [31]. However, the algorithm has some weaknesses, such as the probability of being trapped in the local optimums and slow convergence in some complex engineering problems, so it is necessary to modify the BA.…”

Section: Innovation and Objectivesmentioning

confidence: 99%

Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Ehteram

Othman

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2018

Water

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Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must be determined for accurate flood routing. In this context, an optimization process that self-searches for the optimal values of these four parameters might improve the traditional Muskingum model. In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. Data for the three different case studies from the USA and the UK were utilized to examine the suitability of the proposed HBSA for flood routing. Comparative analyses based on the sum of squared deviations (SSD), sum of absolute deviations (SAD), error of peak discharge, and error of time to peak showed that the proposed HBSA based on the Muskingum model achieved excellent flood routing accuracy compared to that of other methods while requiring less computational time.

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A novel method for image thresholding using interval type-2 fuzzy set and Bat algorithm

Cited by 36 publications

References 38 publications

A Defect Detection Method for the Image With Intersecting Feature

A Defect Detection Method for the Image With Intersecting Feature

Applications of Novel Hybrid Bat Algorithm With Constrained Pareto Fuzzy Dominant Rule on Multi-Objective Optimal Power Flow Problems

Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

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