Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling
This paper presents a novel dynamic Jellyfish Search Algorithm using a Simulated Annealing and disruption operator, called DJSD. The developed DJSD method incorporates the Simulated Annealing operators into the conventional Jellyfish Search Algorithm in the exploration stage, in a competitive manner...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/11/1894 |
| _version_ | 1827664746004873216 |
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| author | Ibrahim Attiya Laith Abualigah Samah Alshathri Doaa Elsadek Mohamed Abd Elaziz |
| author_facet | Ibrahim Attiya Laith Abualigah Samah Alshathri Doaa Elsadek Mohamed Abd Elaziz |
| author_sort | Ibrahim Attiya |
| collection | DOAJ |
| description | This paper presents a novel dynamic Jellyfish Search Algorithm using a Simulated Annealing and disruption operator, called DJSD. The developed DJSD method incorporates the Simulated Annealing operators into the conventional Jellyfish Search Algorithm in the exploration stage, in a competitive manner, to enhance its ability to discover more feasible regions. This combination is performed dynamically using a fluctuating parameter that represents the characteristics of a hammer. The disruption operator is employed in the exploitation stage to boost the diversity of the candidate solutions throughout the optimization operation and avert the local optima problem. A comprehensive set of experiments is conducted using thirty classical benchmark functions to validate the effectiveness of the proposed DJSD method. The results are compared with advanced well-known metaheuristic approaches. The findings illustrated that the developed DJSD method achieved promising results, discovered new search regions, and found new best solutions. In addition, to further validate the performance of DJSD in solving real-world applications, experiments were conducted to tackle the task scheduling problem in cloud computing applications. The real-world application results demonstrated that DJSD is highly competent in dealing with challenging real applications. Moreover, it achieved gained high performances compared to other competitors according to several standard evaluation measures, including fitness function, makespan, and energy consumption. |
| first_indexed | 2024-03-10T01:06:13Z |
| format | Article |
| id | doaj.art-26b43b121cc5428ba50f7115fdf9e293 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T01:06:13Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-26b43b121cc5428ba50f7115fdf9e2932023-11-23T14:26:28ZengMDPI AGMathematics2227-73902022-06-011011189410.3390/math10111894Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task SchedulingIbrahim Attiya0Laith Abualigah1Samah Alshathri2Doaa Elsadek3Mohamed Abd Elaziz4Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, EgyptFaculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, JordanDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, EgyptDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, EgyptThis paper presents a novel dynamic Jellyfish Search Algorithm using a Simulated Annealing and disruption operator, called DJSD. The developed DJSD method incorporates the Simulated Annealing operators into the conventional Jellyfish Search Algorithm in the exploration stage, in a competitive manner, to enhance its ability to discover more feasible regions. This combination is performed dynamically using a fluctuating parameter that represents the characteristics of a hammer. The disruption operator is employed in the exploitation stage to boost the diversity of the candidate solutions throughout the optimization operation and avert the local optima problem. A comprehensive set of experiments is conducted using thirty classical benchmark functions to validate the effectiveness of the proposed DJSD method. The results are compared with advanced well-known metaheuristic approaches. The findings illustrated that the developed DJSD method achieved promising results, discovered new search regions, and found new best solutions. In addition, to further validate the performance of DJSD in solving real-world applications, experiments were conducted to tackle the task scheduling problem in cloud computing applications. The real-world application results demonstrated that DJSD is highly competent in dealing with challenging real applications. Moreover, it achieved gained high performances compared to other competitors according to several standard evaluation measures, including fitness function, makespan, and energy consumption.https://www.mdpi.com/2227-7390/10/11/1894artificial Jellyfish Search Algorithm (JSA)simulated annealing (SA)task schedulingcloud computingoptimizationmetaheuristics |
| spellingShingle | Ibrahim Attiya Laith Abualigah Samah Alshathri Doaa Elsadek Mohamed Abd Elaziz Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling Mathematics artificial Jellyfish Search Algorithm (JSA) simulated annealing (SA) task scheduling cloud computing optimization metaheuristics |
| title | Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling |
| title_full | Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling |
| title_fullStr | Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling |
| title_full_unstemmed | Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling |
| title_short | Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling |
| title_sort | dynamic jellyfish search algorithm based on simulated annealing and disruption operators for global optimization with applications to cloud task scheduling |
| topic | artificial Jellyfish Search Algorithm (JSA) simulated annealing (SA) task scheduling cloud computing optimization metaheuristics |
| url | https://www.mdpi.com/2227-7390/10/11/1894 |
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