A Novel Fuzzy logic based Improved Cuckoo Search Algorithm

Cuckoo Search (CS) algorithm is a nature-inspired optimization algorithm (NIOA) with less control parameters that is stable, versatile, and easy to implement. CS has good global search capabilities, but it is prone to local optima problems. As a result, it may be possible to improve the classic CS algorithm's optimization capability. Centered on fuzzy set theory, this paper introduces an improved CS version. The population of solutions has been divided into two fuzzy sets, and each solution is assigned to one of the sets based on its fitness. The fuzzy collection centroids, global best solution advice, and Lévy distribution dependent mutation are all used to boost the population's solutions. With well-accepted objective functions such as Otsu inter class variance and Kapur's entropy, the experimental analysis has been conducted on the CEC-2014 test suite and image multi-level thresholding domain. The proposed fuzzy cuckoo search (FCS) algorithm is compared to the classical CS, PSO, FA, SMA, and BA algorithm and provides satisfactory results.

A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy

Multilevel thresholding image segmentation has always been a hot issue of research in last several years since it has a plenty of applications. Traditional exhaustive search method consumes a lot of time for searching the optimal multilevel thresholding, color images contain more information, solving multilevel thresholding will become worse. However, the meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Kapur's entropy is considered as its objective function. In the FaFA, a fuzzy logical controller is designed to adjust the control parameters. A total of six satellite remote sensing color images are conducted in the experiments. The performance of the FaFA is compared with FA, BWO, SSA, NaFA and ODFA. Some measure metrics are performed in the experiments. The experimental results show that the FaFA obviously outperforms other five algorithms.

A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy

Multilevel thresholding image segmentation has always been a hot issue of research in last several years since it has a plenty of applications. Traditional exhaustive search method consumes a lot of time for searching the optimal multilevel thresholding, color images contain more information, solving multilevel thresholding will become worse. However, the meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Kapur's entropy is considered as its objective function. In the FaFA, a fuzzy logical controller is designed to adjust the control parameters. A total of six satellite remote sensing color images are conducted in the experiments. The performance of the FaFA is compared with FA, BWO, SSA, NaFA and ODFA. Some measure metrics are performed in the experiments. The experimental results show that the FaFA obviously outperforms other five algorithms.

Kapur's entropy for multilevel thresholding image segmentation based on moth-flame optimization

<abstract> <p>Multilevel thresholding is a reliable and efficacious method for image segmentation that has recently received widespread recognition. However, the computational complexity of the multilevel thresholding method increases as the threshold level increases, which causes the low segmentation accuracy of this method. To overcome this shortcoming, this paper presents a moth-flame optimization (MFO) established on Kapur's entropy to clarify the multilevel thresholding image segmentation. The MFO adjusts exploration and exploitation to achieve the best fitness value. To validate the overall performance, MFO is compared with other algorithms to realize the global optimal solution to maximize the target value of Kapur's entropy. Some critical evaluation indicators are used to determine the segmentation effect and optimization performance of each algorithm. The experimental results indicate that MFO has a faster convergence speed, higher calculation accuracy, better segmentation effect and better stability.</p> </abstract>

A New Harris Hawks-Cuckoo Search Optimizer for Multilevel Thresholding of Thermogram Images

The exploitation capability of the Harris Hawks optimization (HHO) is limited. This problem is solved here by incorporating features of Cuckoo search (CS). This paper proposes a new algorithm called Harris hawks-cuckoo search (HHO-CS) algorithm. The algorithm is validated using 23 Benchmark functions. A statistical analysis is carried out. Convergence of the proposed algorithm is studied. Nonetheless, converting color breast thermogram images into grayscale for segmentation is not effective. To overcome the problem, we suggest an RGB colour component based multilevel thresholding method for breast cancer thermogram image analysis. Here, 8 different images from the Database for Research Mastology with Infrared images are considered for the experiments. Both 1D Otsu’s between-class variance and Kapur's entropy are considered for a fair comparison. Our proposal is evaluated using the performance metrics – Peak Signal to Noise Ratio (PSNR), Feature Similarity Index (FSIM), Structure Similarity Index (SSIM). The suggested method outperforms the grayscale based multilevel thresholding method proposed earlier. Moreover, our method using 1D Otsu’s fitness functions performs better than Kapur’s entropy based approach. The proposal would be useful for analysis of infrared images. Finally, the proposed HHO-CS algorithm may be useful for function optimization to solve real world engineering problems.

Elephant Herding Optimization for Multi-Level Image Thresholding

Multilevel thresholding plays a significant role in the arena of image segmentation. The main issue of multilevel image thresholding is to select the optimal combination of threshold value at different level. However, this problem has become challenging with the higher number of levels, because computational complexity is increased exponentially as the increase of number of threshold. To address this problem, this paper has proposed elephant herding optimization (EHO) based multilevel image thresholding technique for image segmentation. The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as ‘Kapur's entropy' and ‘between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.