Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies

Although the continuous version of ant colony optimizer (ACOR) has been successfully applied to various problems, there is room to boost its stability and improve convergence speed and precision. In addition, it is prone to stagnation, which means it cannot step out of the local optimum (LO). To effectively mitigate these concerns, an improved method using a random spare strategy and chaotic intensification strategy is proposed. Also, its selection mechanism is enhanced in our research. Among the new components, the convergence speed is mainly boosted by using a random spare approach. To effectively augment the ability to step out of LO and to refine the convergence accuracy, the chaotic intensification strategy and improved selection mechanism are applied to ACOR. To better verify the effectiveness of the proposed method, a series of comparative experiments are conducted by using 30 benchmark functions. According to all experimental results, it is evident that the convergence rapidity and accuracy of the proposed method is better than other peers. In addition, it is observed that the capability of enhanced RCACO is more reliable than other techniques in stepping out of LO. Furthermore, an excellent multi-threshold image segmentation method is proposed in this paper. On this basis, image segmentation experiments at low threshold levels and high threshold levels are also respectively carried out. The experimental results also adequately disclose that the segmentation results of RCACO for both multi-threshold image segmentation at a low threshold level and high threshold level, are even more satisfactory compared to other studied algorithms. An online homepage supports this research for access to sharable codes, any question and info about this research at https://aliasgharheidari.com .

Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy

Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This article attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs and hot- and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: statistical, spectral, model-based, and machine learning. These works are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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Automated Visual Defect Detection for Flat Steel Surface: A Survey

The grasshopper optimization algorithm is one of the dominant modern meta-heuristic optimization algorithms. It has been successfully applied to various optimization problems in several fields, including engineering design, wireless networking, machine learning, image processing, control of power systems, and others. We survey the available literature on the grasshopper optimization algorithm, including its modifications, hybridizations, and generalization to the binary, chaotic, and multi-objective cases. We review its applications, evaluate the algorithms, and provide conclusions.

A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications

Recently, a novel virus called COVID-19 has pervasive worldwide, starting from China and moving to all the world to eliminate a lot of persons. Many attempts have been experimented to identify the infection with COVID-19. The X-ray images were one of the attempts to detect the influence of COVID-19 on the infected persons from involving those experiments. According to the X-ray analysis, bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities can be caused by COVID-19 - sometimes with a rounded morphology and a peripheral lung distribution. But unfortunately, the specification or if the person infected with COVID-19 or not is so hard under the X-ray images. X-ray images could be classified using the machine learning techniques to specify if the person infected severely, mild, or not infected. To improve the classification accuracy of the machine learning, the region of interest within the image that contains the features of COVID-19 must be extracted. This problem is called the image segmentation problem (ISP). Many techniques have been proposed to overcome ISP. The most commonly used technique due to its simplicity, speed, and accuracy are threshold-based segmentation. This paper proposes a new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur's entropy. The performance of integrated SMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms: Lshade algorithm, whale optimization algorithm (WOA), FireFly algorithm (FFA), Harris-hawks algorithm (HHA), salp swarm algorithms (SSA), and the standard SMA. The experimental results demonstrate that the proposed algorithm outperforms SMA under Kapur's entropy for all the metrics used and the standard SMA could perform better than the other algorithms in the comparison under all the metrics.

HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images.

Multilevel thresholding has got more attention in recent years with various successful applications. However, the implementation becomes more and more complex and time-consuming when the number of thresholds is high, and color images which contain more information are even worse. Therefore, this paper proposes an alternative hybrid algorithm for color image segmentation, the advantages of which lie in extracting the best features from the high performance of two algorithms and overcoming the limitations of each algorithm to some extent. Two techniques, Otsu's method, and Kapur's entropy, are used as fitness function to determine the segmentation threshold values. Harris hawks optimization (HHO) is a novel and general-purpose algorithm, and the hybridization of HHO is fulfilled by adding another powerful algorithm-differential evolution (DE), which is known as HHO-DE. More specifically, the whole population is divided into two equal subpopulations which will be assigned to HHO and DE algorithms, respectively. Then both algorithms operate in parallel to update the positions of each subpopulation during the iterative process. In order to fully demonstrate the superior performance of HHO-DE, the proposed method is compared with the seven state-of-the-art algorithms by an array of experiments on ten benchmark images. Meanwhile, five measures, including the average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM), are used to evaluate the performance of each algorithm. In addition, Wilcoxon's rank sum test for statistical analysis and the comparison with the super-pixel method are also conducted to verify the superiority of HHO-DE. The experimental results reveal that the proposed method significantly outperforms other algorithms. Hence, the HHO-DE algorithm is a remarkable and promising tool for multilevel thresholding color image segmentation.

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A Novel Hybrid Harris Hawks Optimization for Color Image Multilevel Thresholding Segmentation

Multilevel thresholding is an important approach for image segmentation which has drawn much attention during the past few years. The Tsallis entropy method is implemented for its effectiveness and simplicity. Although it is efficient and gives an excellent result in the case of bi-level thresholding, its evaluation becomes complexity when the number of thresholds increases. To overcome the problem, the metaheuristic algorithms are applied in this search area for searching the optimal thresholds. In this paper, a modified grasshopper optimization algorithm (GOA) is adopted to render multilevel Tsallis cross entropy more practical and reduce the complexity. The Levy flight algorithm is employed to modify the original GOA and balance the exploration and exploitation of the GOA. Experiments are conducted between five state-of-the-art metaheuristic algorithms and the proposed one. In addition, the proposed approach is compared with thresholding techniques depending on between-class variance (Otsu) method and the Renyi entropy function. Both real life images and plant stomata images are used in the experiments to test the performance of the algorithms involved. Qualitative experimental results show that the proposed segmentation approach has a fewer iterations and a higher segmentation accuracy.

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Modified Grasshopper Algorithm-Based Multilevel Thresholding for Color Image Segmentation

The grayscale co-occurrence matrix (GLCM) can be adapted to segment the image according to the pixels, but the segmentation effect becomes worse as the number of threshold increases. To solve this problem, we propose an improved salp swarm algorithm (LSSA) to optimize GLCM, with the novel diagonal class entropy (DCE) as the fitness function of the GLCM algorithm. At the same time, in order to increase the optimization ability of traditional SSA algorithm, Levy flight (LF) strategy should be improved. Through experiments on the LSSA algorithm of the color natural images, the satellite images, and the Berkeley images, the segmentation quality of the segmented images is evaluated by peak signal-to-noise ratio, feature similarity, probability rand index, variation of information, global consistency error, and boundary displacement error. The experimental results show that the segmentation ability of the GLCM-LSSA algorithm is superior to other comparison algorithms and has a good segmentation ability.

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Multilevel Color Image Segmentation Based on GLCM and Improved Salp Swarm Algorithm

The purpose of this paper is to propose a new hybrid metaheuristic to solve the problem of feature selection. Feature selection problem is the process of finding the most relevant subset based on some criteria. A hybrid metaheuristic is a new trend in the development of optimization algorithms. In this paper, two different hybrid models are designed based on spotted hyena optimization (SHO) for feature selection problem. The SHO algorithm can find the optimal or nearly optimal feature subset in the feature space to minimize the given fitness function. In the first model, the simulated annealing algorithm (SA) is embedded in the SHO algorithm (called SHOSA-1) to enhance the optimal solution found by the SHO algorithm after each iteration. In the second model, SA enhances the final solution obtained by the SHO algorithm (called SHOSA-2). The performance of these methods is evaluated in 20 datasets in the UCI repository. The experiments show that SHOSA-1 performs better than the native algorithm and SHOSA-2. And then, SHOSA-1 is compared with six state-of-the-art optimization algorithms. The experimental results confirm that SHOSA-1 improves the classification accuracy and reduces the number of selected features compared with other wrapper-based optimization algorithms. That proves the excellent performance of SHOSA-1 in spatial search and feature attribute selection.

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Spotted Hyena Optimization Algorithm With Simulated Annealing for Feature Selection

Hybrid algorithms have attracted more and more attention in the field of optimization algorithms. In this paper, three hybrid algorithms are proposed to solve feature selection problems based on seagull optimization algorithm (SOA) and thermal exchange optimization (TEO). In the first algorithm, we take the roulette wheel to choose one of the two algorithms for located updating. Another method is to join the TEO algorithm for optimization after SOA algorithm iteration. The last method is to adopt TEO algorithm's heat exchange formula to improve the seagull attack mode of SOA algorithm, so as to improve the exploitation ability of SOA algorithm. The performance of proposed methods is evaluated on 20 standard benchmark datasets in the UCI repository and compared with three well-known hybrid optimization feature selection methods in the literature. The experimental results illustrate that the proposed algorithm has high efficiency in improving classification accuracy, ensuring the ability of hybrid SOA algorithm in feature selection and classification task information attribute selection, and reducing the CPU time.

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Heming Jia

Papers

A Novel Hybrid Harris Hawks Optimization for Color Image Multilevel Thresholding Segmentation

Modified Grasshopper Algorithm-Based Multilevel Thresholding for Color Image Segmentation

Multilevel Color Image Segmentation Based on GLCM and Improved Salp Swarm Algorithm

Spotted Hyena Optimization Algorithm With Simulated Annealing for Feature Selection

A New Hybrid Seagull Optimization Algorithm for Feature Selection