A comprehensive study of cuckoo-inspired algorithms

Nature-inspired metaheuristics model for gene selection and classification of biomedical microarray data

Identifying a small subset of informative genes from a gene expression dataset is an important process for sample classification in the fields of bioinformatics and machine learning. In this process, there are two objectives: first, to minimize the number of selected genes, and second, to maximize the classification accuracy of the used classifier. In this paper, a hybrid machine learning framework based on a nature-inspired cuckoo search (CS) algorithm has been proposed to resolve this problem. The proposed framework is obtained by incorporating the cuckoo search (CS) algorithm with an artificial bee colony (ABC) in the exploitation and exploration of the genetic algorithm (GA). These strategies are used to maintain an appropriate balance between the exploitation and exploration phases of the ABC and GA algorithms in the search process. In preprocessing, the independent component analysis (ICA) method extracts the important genes from the dataset. Then, the proposed gene selection algorithms along with the Naive Bayes (NB) classifier and leave-one-out cross-validation (LOOCV) have been applied to find a small set of informative genes that maximize the classification accuracy. To conduct a comprehensive performance study, proposed algorithms have been applied on six benchmark datasets of gene expression. The experimental comparison shows that the proposed framework (ICA and CS-based hybrid algorithm with NB classifier) performs a deeper search in the iterative process, which can avoid premature convergence and produce better results compared to the previously published feature selection algorithm for the NB classifier.

Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio (PSNR), structure similarity (SSIM), and feature similarity (FSIM). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation.