Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization

This paper presents a novel bio-inspired optimization algorithm called Rat Swarm Optimizer (RSO) for solving the challenging optimization problems. The main inspiration of this optimizer is the chasing and attacking behaviors of rats in nature. This paper mathematically models these behaviors and benchmarks on a set of 38 test problems to ensure its applicability on different regions of search space. The RSO algorithm is compared with eight well-known optimization algorithms to validate its performance. It is then employed on six real-life constrained engineering design problems. The convergence and computational analysis are also investigated to test exploration, exploitation, and local optima avoidance of proposed algorithm. The experimental results reveal that the proposed RSO algorithm is highly effective in solving real world optimization problems as compared to other well-known optimization algorithms. Note that the source codes of the proposed technique are available at: http://www.dhimangaurav.com
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A novel algorithm for global optimization: Rat Swarm Optimizer

Emperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently developed and illustrates the emperor penguin’s huddling behaviour. However, the original version of the EPO will fix issues that are continuing in fact but not discrete. The eight separate EPO variants have been provided in this article. Four transfer features, s-shaped and v-shaped, that are used in order to map the search space into a separate research space are considered in the proposed algorithm. The output of the proposed algorithm is validated using 25 standard benchmark functions. It also analyses the statistical sense of the proposed algorithm. Experimental findings and comparisons suggest that the proposed algorithm performs better than other algorithms. The solution also applies to the issue of feature selection. The findings reveal the supremacy of the binary emperor penguin optimization algorithm.

BEPO: A novel binary emperor penguin optimizer for automatic feature selection

This study introduces the extension of currently developed Seagull Optimization Algorithm (SOA) in terms of multi-objective problems, which is entitled as Multi-objective Seagull Optimization Algorithm (MOSOA). In this algorithm, a concept of dynamic archive is introduced, which has the feature to cache the non-dominated Pareto optimal solutions. The roulette wheel selection approach is utilized to choose the effective archived solutions by simulating the migration and attacking behaviors of seagulls. The proposed algorithm is approved by testing it with twenty-four benchmark test functions, and its performance is compared with existing metaheuristic algorithms. The developed algorithm is analyzed on six constrained problems of engineering design to assess its appropriateness for finding the solutions of real-world problems. The outcomes from the empirical analyzes depict that the proposed algorithm is better than other existing algorithms. The proposed algorithm also considers those Pareto optimal solutions, which demonstrate high convergence.

MOSOA: A new multi-objective seagull optimization algorithm

This paper presents a content-based image retrieval technique that focuses on extraction and reduction in multiple features. To obtain multi-level decomposition of the image by extracting approximation and correct coefficients, discrete wavelet transformation is applied to the RGB channels initially. Therefore, both approximation and correct coefficients are applied to the dominant rotated local binary pattern termed as texture descriptor which is computationally effective and rotationally invariant. For a local neighbor patch, a rotation invariance function image is obtained by measuring the descriptor relative to the reference. The proposed approach contains the complete structural information extracted from the local binary patterns and also extracts the additional information using the information of magnitude, thereby achieving extra discriminative power. Then, GLCM description is used by obtaining the dominant rotated local binary pattern image to extract the statistical characteristics for texture image classification. The proposed technique is applied to CORAL dataset with the help of particle swarm optimization-based feature selector to minimize the number of features that can be used during the classification process. The three classifiers, i.e., support vector machine, K-nearest neighbor, and decision tree, are trained and tested. The comparison is based in terms of Accuracy, precision, recall, and F-measure performance metrics for classification. Experimental results show that the proposed approach achieves better accuracy, precision, recall, and F-measure values for most of the CORAL dataset classes.

A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants

Defect Inspection of Textured Surfaces is a challenging problem which occurs during manufacturing in many processing phases. With arbitrary length, shape and orientation, these defects occur. Moreover, there are fewer and different photos of defective products. Deep Convolution Neural Network (CNN) has an impressive development in target detection, and better results have been obtained with the implementation of deep CNN design for texture detection. Nonetheless, with the growing detection accuracy of deep CNNs, there are the drawbacks of significantly increasing computational costs and processing resources, which seriously hinders CNN's use in resource-limited environments such as mobile or embedded phones. In this paper, a novel framework is proposed that uses raw image database patch statistics joint with two layers of neural network for surface defect detection. For defect detection, a convolution neural network (CNN) classifier is used. Imaging analysis of training samples using Deep Convolution Neural Network (CNN) is used to find the defect in an image's target area. In point of energy saving, the results of the experiment show that proposed method has numerous advantages in terms of reduction in time and cost. It also shows the high-performance contrast to conventional manual inspection process with less repetition and helps to build the object detection classifier with high generalization potential and high detection accuracy.

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Deep Convolution Neural Network Approach for Defect Inspection of Textured Surfaces

Image retrieval based on content not only relies heavily upon the type of descriptors, but on the steps taken further. This has been an extensively utilized methodology for finding and fetching out images from the big database of images. Nowadays, a number of methodologies have been organized to increase the CBIR performance. This has an ability to recover pictures relying upon their graphical information. In the proposed method, Neuro-Fuzzy classifier and Deep Neural Network classifier are used to classify the pictures from a given dataset. The proposed approach obtained the highest accuracy in terms of Precision, Recall, and F-measure. To show the efficiency and effectiveness of proposed approach, statistical testing is used in terms of standard deviation, skewness, and kurtosis. The results reveal that the proposed algorithm outperforms other approaches using low computational efforts.

Fuzzy-NN approach with statistical features for description and classification of efficient image retrieval

The Publisher has retracted this article because it was published in error due to an administrative mistake. The article is also not within the journal's scope and was erroneously published due to ...

Meenakshi Garg

Papers

A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants

Deep Convolution Neural Network Approach for Defect Inspection of Textured Surfaces

Fuzzy-NN approach with statistical features for description and classification of efficient image retrieval

Comparison of deep learning techniques on content based image retrieval