Cat swarm optimization for solving the open shop scheduling problem

Bouzidi, Abdelhamid; Riffi, Mohammed Essaid; Barkatou, Mohammed

doi:10.1007/s40092-018-0297-z

Cited by 42 publications

(22 citation statements)

References 35 publications

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“…Comparison of the proposed algorithm (BA_OS) with other algorithms is presented in Tables 9 and 10. The proposed algorithm is compared with simulated algorithm (SA) [51], GA [52,53] HGA [52,54], neural networks [55], ant colony system (ACS) [56], cuckoo search (CS) [56] and cat swarm optimization algorithm (CSO) [57]. As seen, proposed algorithm could solve the problem well and compete with compared algorithms.…”

Section: Simulation Resultsmentioning

confidence: 99%

An improved bat optimization algorithm to solve the tasks scheduling problem in open shop

Shareh

Bargh

Hosseinabadi

et al. 2020

Neural Comput & Applic

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The open shop scheduling problem involves a set of activities that should be run on a limited set of machines. The purpose of scheduling open shops problem is to provide a timetable for implementation of the entire operation so that the total execution time is reduced. The tasks scheduling problem in open shops is important in many applications due to the arbitrariness of the processing sequence of each job and lack of a prioritization of its operations. This is an NP-hard problem and obtaining an optimal solution to this problem requires a high time complexity. Therefore, heuristic techniques are used to solve these problems. In this paper, we investigate the tasks scheduling problem in open shops using the Bat Algorithm (BA) based on ColReuse and substitution meta-heuristic functions. The heuristic functions are designed to increase the rate of convergence to the optimal solution. To evaluate the performance of the proposed algorithm, standard open shop benchmarks were used. The results obtained in each benchmark are compared with those of the previous methods. Finally, after analyzing the results, it was found that the proposed BA had a better performance and was able to generate the best solution in all cases.

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Section: Simulation Resultsmentioning

confidence: 99%

An improved bat optimization algorithm to solve the tasks scheduling problem in open shop

Shareh

Bargh

Hosseinabadi

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…[71] Applied BCSO on JSSP ACO outperformed CSO and cuckoo search algorithms [72] Applied CSO on FSSP Carlier, Heller, and Reeves benchmark instances were used, CSO can solve problems of up to 50 jobs accurately [73] Applied CSO on OSSP CSO performs better than six metaheuristic algorithms in the literature. [74] Applied CSO on JSSP CSO performs better than some conventional algorithms in terms of accuracy and speed. [75] Applied CSO on bag-of-tasks and workflow scheduling problems in cloud systems CSO performs better than PSO and two other heuristic algorithms [76] Applied CSO on TSP and QAP e benchmark instances were taken from TSPLIB and QAPLIB.…”

Section: Purpose Resultsmentioning

confidence: 99%

“…en, they used the CSO algorithm to solve flow shop scheduling (FSSP) [73] and open shop scheduling problems (OSSP) as well [74]. Moreover, Dani et al also applied CSO algorithm on JSSP in which they used a nonconventional approach to represent cat positions [75].…”

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

100

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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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“…They also made a comparative study between CSO and two other meta-heuristic algorithms namely: Cuckoo search algorithm (CS), and the Ant Colony Optimization (ACO) for JSSP in [73]. Then, they used the CSO algorithm to solve Flow shop scheduling (FSSP) [74] and open shop scheduling problems (OSSP) as well [75]. Moreover, Dani et al also applied the CSO algorithm on JSSP in which they used a non-conventional approach to represent cat positions [76].…”

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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Cat swarm optimization for solving the open shop scheduling problem

Cited by 42 publications

References 35 publications

An improved bat optimization algorithm to solve the tasks scheduling problem in open shop

An improved bat optimization algorithm to solve the tasks scheduling problem in open shop

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

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