Discrete cat swarm optimization algorithm applied to combinatorial optimization problems

Bouzidi, Abdelhamid; Riffi, Mohammed Essaid

doi:10.1109/wcccs.2014.7107914

Cited by 10 publications

(7 citation statements)

References 27 publications

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“…e results show that CSO outperformed the best results recorded in those dataset references. [77] Comparison between CSO, cuckoo search, and batinspired algorithm to solve TSP problem e benchmark instances are taken from STPLIB. e results show that CSO falls behind the other algorithms [78] Applied CSO and MCSO on workflow scheduling in cloud systems CSO performs better than PSO [79] Applied BCSO on workflow scheduling in cloud systems BCSO performs better than PSO and BPSO [80] Applied BCSO on SCP BCSO performs better than ABC [81] Applied BCSO on SCP BCSO performs better than binary teaching-learningbased optimization (BTLBO) [82,83] Used a CSO as a clustering mechanism in web services.…”

Section: Purpose Resultsmentioning

confidence: 99%

“…Maurya and Tripathi also applied CSO algorithm on bag-of-tasks and workflow scheduling problems in cloud systems [76]. Bouzidi and Riffi applied CSO algorithm on the traveling salesman problem (TSP) and the quadratic assignment problem (QAP), which are two combinatorial optimization problems [77]. Bouzidi et al also made a comparative study between CSO algorithm, cuckoo search algorithm, and batinspired algorithm for addressing TSP [78].…”

Section: System Management and Combinatorial Optimizationmentioning

confidence: 99%

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Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed

Rashid

Saeed

2020

Computational Intelligence and Neuroscience

101

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This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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Section: Purpose Resultsmentioning

confidence: 99%

Section: System Management and Combinatorial Optimizationmentioning

confidence: 99%

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed

Rashid

Saeed

2020

Computational Intelligence and Neuroscience

101

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“…To integrate these two modes in the CSO algorithm, a mixture ratio (MR) was defined. Each cat in the algorithm is characterized by its position, velocity, and a flag that specifies the mode it is in [14,35,36]. The seeking and tracing modes are further explained in the following subsections.…”

Section: The Proposed Modified Distance Cat Swarm Optimization For Da...mentioning

confidence: 99%

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

Yousif,

Shohdy,

Hassan

et al. 2023

Electronics

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IoT cloud computing provides all functions of traditional computing as services through the Internet for the users. Big data processing is one of the most crucial advantages of IoT cloud computing. However, IoT cloud job scheduling is considered an NP-hard problem due to the hardness of allocating the clients’ jobs to suitable IoT cloud provider resources. Previous work on job scheduling tried to minimize the execution time of the job scheduling in the IoT cloud, but it still needs improvement. This paper proposes an enhanced job scheduling mechanism using cat swarm optimization (CSO) with modified distance to minimize the execution time. The proposed job scheduling mechanism first creates a set of jobs and resources to generate the population by randomly assigning the jobs to resources. Then, it evaluates the population using the fitness value, which represents the execution time of the jobs. In addition, we use iterations to regenerate populations based on the cat’s behaviour to produce the best job schedule that gives the minimum execution time for the jobs. We evaluated the proposed mechanism by implementing an initial simulation using Java Language and then conducted a complete simulation using the CloudSim simulator. We ran several experimentation scenarios using different numbers of jobs and resources to evaluate the proposed mechanism regarding the execution time. The proposed mechanism significantly reduces the execution time when we compare the proposed mechanism against the firefly algorithm and glowworm swarm optimization. The average execution time of the proposed cat swarm optimization was 131, while the average execution times for the firefly algorithm and glowworm optimization were 237 and 220, respectively. Hence, the experimental findings demonstrated that the proposed mechanism performs better than the firefly algorithm and glowworm swarm optimization in reducing the execution time of the jobs.

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“…Bouzidi, A. and M.E. Riffi applied CSO Algorithm on the Traveling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP), which are two combinatorial optimization problems [78]. Bouzidi et al also made a comparative study between CSO algorithm, cuckoo search algorithm, and bat-inspired algorithm for addressing TSP [79].…”

Section: 3mentioning

confidence: 99%

“…The results show that CSO outperformed the best results recorded in those dataset references. [78] Comparison between CSO, chukoo search and bat-inspired algorithm to solve TSP problem…”

Section: Computer Vision: Facial Emotionmentioning

confidence: 99%

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Ahmed¹,

Rashid²,

Saeed³

2020

Preprint

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This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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Discrete cat swarm optimization algorithm applied to combinatorial optimization problems

Cited by 10 publications

References 27 publications

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

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