A Novel Crow Swarm Optimization Algorithm (CSO) Coupling Particle Swarm Optimization (PSO) and Crow Search Algorithm (CSA)

Jia, Yinghui; Qiu, Jun; Ma, Zhuangzhuang; Li, Fangfang

doi:10.1155/2021/6686826

Cited by 15 publications

(5 citation statements)

References 24 publications

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“…In the iteration, crow i tracks a random crow j. When crow j isn't aware of being traced (that is., r j ≥ AP), crow i approaches the hiding place pb t j of crow j; if the j crows know that i crow is tracing it, crow j fools i crow by going to random place in the searching space to keep its hiding places from being detected [23].…”

Section: Image Classificationmentioning

confidence: 99%

Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

Albraikan¹,

Nemri²,

Alkhonaini³

et al. 2023

Computers, Materials &Amp; Continua

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Melanoma remains a serious illness which is a common form of skin cancer. Since the earlier detection of melanoma reduces the mortality rate, it is essential to design reliable and automated disease diagnosis model using dermoscopic images. The recent advances in deep learning (DL) models find useful to examine the medical image and make proper decisions. In this study, an automated deep learning based melanoma detection and classification (ADL-MDC) model is presented. The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma. The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage. Besides, the k-means clustering technique is applied for the image segmentation process. In addition, Adagrad optimizer based Capsule Network (CapsNet) model is derived for effective feature extraction process. Lastly, crow search optimization (CSO) algorithm with sparse autoencoder (SAE) model is utilized for the melanoma classification process. The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance. A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects. The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.

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Section: Image Classificationmentioning

confidence: 99%

Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

Albraikan¹,

Nemri²,

Alkhonaini³

et al. 2023

Computers, Materials &Amp; Continua

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show abstract

“…This novel algorithm can improve the quality of solutions. Jia et al also hybridized PSO and CSA [152], where hybrid algorithm (i.e., CSO) was used for several benchmark functions, including both unimodal and multimodal problems with different variable dimensions. Another hybrid algorithm, combining PSO with CSA, was proposed by Das [153].…”

Section: F Research Progress Regarding Theory and Applications Of Cro...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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“…PSO has a strong local search ability, but the ability to explore unknown areas is weak, and CSA is just the opposite. Therefore, FICSA introduces the ability to use known information which can better balance the ability to explore unknown areas and the ability to converge later [26]. In the FICSA, the speed formula of crow tracking is modified, and the information exchange between teams in the PSO is added.…”

Section: Updating Populationmentioning

confidence: 99%

Crow Search Algorithm Based on Information Interaction for Epistasis Detection

Zhang

Shang

et al. 2023

Preprint

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Background: In the genome-wide association study, the interactions of single nucleotide polymorphisms (SNPs) play an important role in revealing the genetic mechanism of complex diseases, and such interaction is called epistasis or epistatic interactions. In recent years, swarm intelligence methods have been widely used to detect epistatic interactions because they can effectively deal with global optimization problems. Results: In this study, we propose a crow search algorithm based on information interaction (FICSA) to detect epistatic interactions. FICSA combines particle swarm optimization (PSO) and crow search algorithm (CSA) to balance the exploration and exploitation in the search process, which can effectively improve the ability of the algorithm to detect epistatic interactions. In addition, opposition-based learning strategy and adaptive parameters are used to further improve the performance of the algorithm. We compare FICSA with other five epistasis detection algorithms on simulated datasets and an age-related macular degeneration (AMD) dataset. The results on simulated datasets show that FICSA has better detection power, while the results on the real dataset demonstrate the effectiveness of the proposed algorithm. Conclusions: The results show that FICSA is better than other methods and can effectively detect epistatic interactions. In addition，FICSA was tested on AMD data, many of the epistatic interactions found have been proved to be related to AMD in the relevant literature. Therefore, FICSA has good performance in epistasis detection.

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A Novel Crow Swarm Optimization Algorithm (CSO) Coupling Particle Swarm Optimization (PSO) and Crow Search Algorithm (CSA)

Cited by 15 publications

References 24 publications

Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

A survey on several new popular swarm intelligence optimization algorithms

Crow Search Algorithm Based on Information Interaction for Epistasis Detection

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