Optimum laplacian wavelet mask based medical image using hybrid cuckoo search – grey wolf optimization algorithm

Daniel, Esther; Anitha, J.; Gnanaraj, J. Paul Richardson

doi:10.1016/j.knosys.2017.05.017

Cited by 72 publications

(26 citation statements)

References 31 publications

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“…GWO is also combined with PSO to suggest some new hybrid versions for performance improvement [71], [72]. Furthermore, cuckoo search is hybridized with GWO [73]. To minimize potential energy function, GA is hybridized with GWO in [74].…”

Section: Combination Of Other Search Techniques With Gwo (Hybridizmentioning

confidence: 99%

Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization

et al. 2020

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Grey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing algorithms. However, the relevant literature identified that the primary GWO due to its position update equation shows superiority in exploitation, but is inefficient in exploration. It shows slow convergence and low precision, too. Motivated by the outlined issues in the primary GWO, this work presents two new and improved GWO algorithms. The first proposed variant modifies all the three models, encircling model of prey, position update equation and the hunting equation of canonical GWO. Further, a new parameter is introduced to scale the encircling and position update equations. As a result, the exploration issue of the algorithm is tackled. Unlike the first variant, the second proposed variant does not modify the position update models, but it incorporates Minkowski's information into GWO. To the best of our knowledge, no such modifications to GWO have been done before. The proposed modified versions of GWO are tested on a well-known test functions suit and then compared with different population-based algorithms, including fast evolutionary programming and particle swarm optimization. It was identified from the simulation results that proposed algorithms outperform different algorithms in comparison on majority of problems. The sensitivity study of the proposed algorithms to their various parameters is also provided. INDEX TERMS Population-based search approaches, evolutionary computation, unconstrained optimization, grey wolf optimization, global search, Minkowski's formula. The associate editor coordinating the review of this manuscript and approving it for publication was Huaqing Li.

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Section: Combination Of Other Search Techniques With Gwo (Hybridizmentioning

confidence: 99%

Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization

et al. 2020

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“…In Paramanandham and Rajendiran, 13 the authors deploy an infrared and panchromatic images fusion technique based on the discrete cosine transform in which the weight coefficients are computed by using PSO. A multimodal medical image fusion method based on the optimum Laplacian wavelet mask which uses a hybrid CSA-Gray Wolf Optimization algorithm-is proposed in Daniel et al 14 In Mohapatra et al, 15 an improved CSA is used to pretrain the extreme learning machine for a binary medical data sets classifier. In Alswaitti et al, 16 an improved approach for PSO-based data clustering is proposed.…”

Section: A Comparison Of Nature-inspired Optimization Algorithmsmentioning

confidence: 99%

A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration

Bejinariu

Costin

2018

Methods Inf Med

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Computational Intelligence Re-meets Medical Image Processing Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases Background In the last decades, new optimization methods based on the nature's intelligence were developed. These metaheuristics can find a nearly optimal solution faster than other traditional algorithms even for high-dimensional optimization problems. All these algorithms have a similar structure, the difference being made by the strategies used during the evolutionary process. Objectives A set of three nature-inspired algorithms, including Cuckoo Search algorithm (CSA), Particle Swarm Optimization (PSO), and Multi-Swarm Optimization (MSO), are compared in terms of strategies used in the evolutionary process and also of the results obtained in case of particular optimization problems. Methods The three algorithms were applied for biomedical image registration (IR) and compared in terms of performances. The expected geometric transform has seven parameters and is composed of rotation against a point in the image, scaling on both axis with different factors, and translation. Results The evaluation consisted of 25 runs of each IR procedure and revealed that (1) PSO offers the most precise solutions; (2) CSA and MSO are more stable in the sense that their solutions are less scattered; and (3) MSO and PSO have a higher convergence speed. Conclusions The evaluation of PSO, MSO, and CSA was made for multimodal IR problems. It is possible that for other optimization problems and also for other settings of the optimization algorithms, the results can be different. Therefore, the nature-inspired algorithms demonstrated their efficacy for this class of optimization problems.

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“…For example, CS has been applied to face recognition [45] and image segmentation such as color image segmentation [46], multilevel color image segmentation [52], and multilevel thresholding for satellite image segmentation [63]. CS has also been used to design the optimum wavelet mask for medical image processing [13].…”

Section: Signal and Image Processingmentioning

confidence: 99%