Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Ahmed, Aram Mahmood; Rashid, Tarik A.; Saeed, Soran

doi:10.36227/techrxiv.11656458

Cited by 18 publications

(11 citation statements)

References 103 publications

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“…QMPSO: QMPSO is a new hybrid metaheuristic algorithm combining modified PSO and improved Q-learning algorithms used for load balancing in a cloud environment [30]. CSO: CSO is a metaheuristic algorithm that belongs to a swarm intelligence family and is based on the natural behavior of cats [31]. D-ACOELB: D-ACOELB is a metaheuristic algorithm based on ACO algorithm used for load balancing in cloud [65].…”

Section: Results and Analysismentioning

confidence: 99%

“…The proposed approach not only focuses on achieving the best classification accuracy among baselines but also efficiently performs scheduling over competitor baselines such as ACOPS [28], CPSO [29], QMPSO [30], CSO [31] and D-ACOELB [65]. All these algorithms are used for achieving load balancing and have performed reasonably well in many approaches.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

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Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q-learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness.

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

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“…Trajectory-based and population-based algorithms are being developed. In trajectory-based algorithms, only one agent is employed to search for the optimal solution, whereas in population-based algorithms multiple agents are used to search for the solution [25]. Cat Swarm Optimisation (CSO) is one such population-based algorithm proposed by Chu et al [26].…”

Section: Cat Swarm Optimization (Cso)mentioning

confidence: 99%

Diagnosis of Keratoconus with Corneal Features Obtained through LBP, LDP, LOOP and CSO

Subramanian

Ramesh

2021

Trends Sci

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Keratoconus, by its name, is the condition of the eye wherein the cornea assumes a conical shape due to the thinning and protrusion of the cornea. Keratoconus, though bilateral, can be asymmetric in that it can progress differently in the eyes of the patient. Keratoconus can start from early adulthood and progress till the age of 40. Early detection of keratoconus is vital in preventing vision loss or costly repairs. The diagnostic tools available range from keratoscope to videokeratoscope but involves human efforts and thereby human errors. Automatic detection of keratoconus is required for large screening camps. With the advances in artificial intelligence techniques for medical diagnosis, new algorithms and techniques have been developed for the early and rapid screening of keratoconus, which aids clinicians in fast diagnosis. Artificial Neural Networks, Support Vector Machines, Radial Basis Function Neural Networks, Decision Trees, Computational Neural Networks, and various optimisation techniques have been used in different studies. The progression of keratoconus is identified by analyzing the shape of the cornea with Local Binary Pattern (LBP), Local Directional Pattern (LDP), Local Optimal Oriented Pattern (LOOP), and Cat Swarm Optimization (CSO) to detect the changes in cornea edges. The image processing with the CSO algorithm optimizes the result for the changes in the cornea and keratoconus detection. A new automated solution for detecting keratoconus is presented that employs texture analysis techniques such as LBP, LDP, LOOP, and CSO. The CSO extracts morphological and granular features from images of the cornea. The proposed method can be used to detect keratoconus by identifying the cornea shape change and improving clinical decisions. Further research can be in the way of grading the level of keratoconus. HIGHLIGHTS Extraction of corneal features for Diagnosis of Keratoconus from corneal topography images Feature vector extraction from corneal images using Local Binary Pattern, Local Direction Pattern and Local Optimal Oriented Pattern Optimisation using Cat Swarm Optimisation GRAPHICAL ABSTRACT

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“…Exploitation uses information that has been collected so far to achieve a solution better than the previous one in the next generation [17], [18]. In contrast, exploration helps the algorithm avoid local optima and obtaining a new solution that can be far from the current solution [19], [20]. Therefore, avoiding local optima is the advantage of exploration while having slow convergence is the drawback of it.…”

Section: Introductionmentioning

confidence: 99%

Chaotic fitness-dependent optimizer for planning and engineering design

Mohammed¹,

Rashid

2021

Soft Comput

Self Cite

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Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that mimics the reproduction behavior of the bee swarm in finding better hives. This algorithm is similar to Particle Swarm Optimization (PSO) but it works differently. The algorithm is very powerful and has better results compared to other common metaheuristic algorithms. This paper aims at improving the performance of FDO, thus, the chaotic theory is used inside FDO to propose Chaotic FDO (CFDO). Ten chaotic maps are used in the CFDO to consider which of them are performing well to avoid local optima and finding global optima. New technic is used to conduct population in specific limitation since FDO technic has a problem to amend population. The proposed CFDO is evaluated by using 10 benchmark functions from CEC2019. Finally, the results show that the ability of CFDO is improved. Singer map has a great impact on improving CFDO while the Tent map is the worst. Results show that CFDO is superior to GA, FDO, and CSO. Both CEC2013 and CEC2005 are used to evaluate CFDO.Finally, the proposed CFDO is applied to classical engineering problems, such as pressure vessel design and the result shows that CFDO can handle the problem better than WOA, GWO, FDO, and CGWO. Besides, CFDO is applied to solve the task assignment problem and then compared to the original FDO. The results prove that CFDO has better capability to solve the problem.

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Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Cited by 18 publications

References 103 publications

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

Diagnosis of Keratoconus with Corneal Features Obtained through LBP, LDP, LOOP and CSO

Chaotic fitness-dependent optimizer for planning and engineering design

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