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This paper introduces a new variant of the metaheuristic algorithm based on the naked mole rat (NMR) algorithm, called the Q-learning naked mole rat algorithm (QL-NMR), for substitution box construction and optimization. Unlike most competing works (which typically integrate a single chaotic map into a particular metaheuristic algorithm), QL-NMR assembles five chaotic maps (i.e., Chebyshev, logistic, circle, Singer, and sinusoidal) as part of the algorithm itself. Using a Q-learning table, QL-NMR remembers the historical performance of each chaotic map during the S-box construction process allowing just-in-time adaptive selection based on its current performance. Experimental results for 8 × 8 S-box generation demonstrate that the proposed QL-NMR gives competitive performance against other existing works, particularly in terms of nonlinearity and strict avalanche criteria. To further demonstrate the effectiveness of our proposed work, we have subjected the QL-NMR for image segmentation using multilevel thresholding. The results confirm that QL-NMR gives better performance than its predecessor NMR. Finally, QL-NMR S-box also outperformed NMR S-box in image encryption. [ABSTRACT FROM AUTHOR]

Segmentation is considered as one of the most significant tasks in image processing. It consists of separating the pixels into different segments based on their intensity level according to threshold values. Selecting the optimal threshold value is the key to best quality segmentation. Multilevel thresholding (MT) is an essential approach for image segmentation, and it has become very popular during the past few years, but while increasing the level of thresholds, computational complexity also increases exponentially. In order to overcome this drawback, several metaheuristics-based algorithms have been used for determining the optimal MT levels. Learning enthusiasm-based teaching–learning-based optimization (LebTLBO) is a recently developed efficient, simple-to-implement and computationally inexpensive algorithm. It simulates the behaviors of the teaching and learning process in a classroom and gives the probability of getting the amount of information by the learner (student) from the educator. In this paper, LebTLBO is applied on ten standard test images having a diverse histogram, which are taken from Berkeley Segmentation Dataset 500 (BSDS500) (Martin et al. in a database of human segmented natural images and its application to evaluate segmentation algorithms and measure ecological statistics, 2001) benchmark image set for segmentation. The search capability of the algorithm is combined with Otsu and Kapur's entropy MT objective functions for image segmentation. The proposed approach is compared with the existing state-of-the-art optimization algorithms such as MTEMO, GA, PSO and BF for both Otsu and Kapur's entropy methods. Qualitative experimental outcomes demonstrate that LebTLBO is highly efficient in terms of performance metrics such as PSNR, mean, threshold values, number of iterations taken to converge and image segmentation quality. [ABSTRACT FROM AUTHOR]

Multilevel thresholding for image segmentation is one of the crucial techniques in image processing. Even though numerous methods have been proposed in literature, it is still a challenge for the existing methods to produce steady satisfactory thresholds at manageable computational cost in segmenting images with various unknown properties. In this paper, a non-revisiting quantum-behaved particle swarm optimization (NrQPSO) algorithm is proposed to find the optimal multilevel thresholds for gray-level images. The proposed NrQPSO uses the non-revisiting scheme to avoid the re-evaluation of the evaluated solution candidates. To reduce the unnecessary computation cost, the NrQPSO provides an automatic stopping mechanism which is capable of gauging the progress of exploration and stops the algorithm rationally in a natural manner. For further improving the computation efficiency, the NrQPSO employs a meticulous solution search method to overcome the drawback of the existing QPSO algorithms using the original search methods. Performance of the NrQPSO is tested on the Berkeley segmentation data set. The experimental results have demonstrated that the NrQPSO can outperform the other state-of-the-art population-based thresholding methods in terms of efficiency, effectiveness and robustness; thus, the NrQPSO can be applied in real-time massive image processing. [ABSTRACT FROM AUTHOR]

The speed and accuracy with which the patient affected with brain tumour is diagnosed and monitored, plays a very crucial role in providing treatment to the patient. During the diagnosis of the diseased part, a constant demand is anticipated to easily extract the specific region of interest within the complex medical image. This task of extracting only the diseased portion amid the complex body parts in the complex medical image can be achieved by image segmentation. Accuracy and speed of extracting the points or area of interest within the multipart medical image can be improved by using various evolutionary techniques. Differential evolution (DE) is an efficient evolutionary technique that can be used for solving optimisation problem like image segmentation. The main disadvantage of classical evolutionary technique is its inability to adapt its solution algorithm to a given problem. Owing to this need, more adaptable and flexible algorithms are in demand. Numerous variants of DE exist which differ in their solutions. Here, a variant of differential evolution named as transformed differential evolution (TDE) is presented which has an improved mutation strategy that is optimised to fewer function evaluations. This variant is combined with the Kapur's multi-level thresholding for segmenting magnetic resonance imaging (MRI) images and to extract only the regions of tumour. The results obtained using TDE with Kapur's multi-level thresholding were compared with the results using traditional Kapur's technique and the new results improved profoundly. By introducing TDE in multilevel thresholding, the computational time significantly reduced and the resultant image quality improved greatly. [ABSTRACT FROM AUTHOR]