Taxonomy of memory usage in swarm intelligence-based metaheuristics

Metaheuristics under the swarm intelligence (SI) class have proven to be efficient and have become popular methods for solving different optimization problems. Based on the usage of memory, metaheuristics can be classified into algorithms with memory and without memory (memory-less). The absence of...

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Main Authors: Yasear, Shaymah Akram, Ku-Mahamud, Ku Ruhana
Format: Article
Language:English
Published: University of Baghdad 2019
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Online Access:http://repo.uum.edu.my/27864/1/BSJ%2016%20SI%202019%20445%20452.pdf
http://repo.uum.edu.my/27864/
http://doi.org/10.21123/bsj.2019.16.2(SI).0445
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Institution: Universiti Utara Malaysia
Language: English
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spelling my.uum.repo.278642020-11-10T05:55:43Z http://repo.uum.edu.my/27864/ Taxonomy of memory usage in swarm intelligence-based metaheuristics Yasear, Shaymah Akram Ku-Mahamud, Ku Ruhana QA75 Electronic computers. Computer science Metaheuristics under the swarm intelligence (SI) class have proven to be efficient and have become popular methods for solving different optimization problems. Based on the usage of memory, metaheuristics can be classified into algorithms with memory and without memory (memory-less). The absence of memory in some metaheuristics will lead to the loss of the information gained in previous iterations. The metaheuristics tend to divert from promising areas of solutions search spaces which will lead to non-optimal solutions. This paper aims to review memory usage and its effect on the performance of the main SI-based metaheuristics. Investigation has been performed on SI metaheuristics, memory usage and memory-less metaheuristics, memory characteristics and memory in SI-based metaheuristics. The latest information and references have been further analyzed to extract key information and mapped into respective subsections. A total of 50 references related to memory usage studies from 2003 to 2018 have been investigated and show that the usage of memory is extremely necessary to increase effectiveness of metaheuristics by taking the advantages from their previous successful experiences. Therefore, in advanced metaheuristics, memory is considered as one of the fundamental elements of an efficient metaheuristic. Issues in memory usage have also been highlighted. The results of this review are beneficial to the researchers in developing efficient metaheuristics, by taking into consideration the usage of memory. University of Baghdad 2019 Article PeerReviewed application/pdf en http://repo.uum.edu.my/27864/1/BSJ%2016%20SI%202019%20445%20452.pdf Yasear, Shaymah Akram and Ku-Mahamud, Ku Ruhana (2019) Taxonomy of memory usage in swarm intelligence-based metaheuristics. Baghdad Science Journal, 16 (2(SI)). pp. 445-452. ISSN 2078-8665 http://doi.org/10.21123/bsj.2019.16.2(SI).0445 doi:10.21123/bsj.2019.16.2(SI).0445
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Yasear, Shaymah Akram
Ku-Mahamud, Ku Ruhana
Taxonomy of memory usage in swarm intelligence-based metaheuristics
description Metaheuristics under the swarm intelligence (SI) class have proven to be efficient and have become popular methods for solving different optimization problems. Based on the usage of memory, metaheuristics can be classified into algorithms with memory and without memory (memory-less). The absence of memory in some metaheuristics will lead to the loss of the information gained in previous iterations. The metaheuristics tend to divert from promising areas of solutions search spaces which will lead to non-optimal solutions. This paper aims to review memory usage and its effect on the performance of the main SI-based metaheuristics. Investigation has been performed on SI metaheuristics, memory usage and memory-less metaheuristics, memory characteristics and memory in SI-based metaheuristics. The latest information and references have been further analyzed to extract key information and mapped into respective subsections. A total of 50 references related to memory usage studies from 2003 to 2018 have been investigated and show that the usage of memory is extremely necessary to increase effectiveness of metaheuristics by taking the advantages from their previous successful experiences. Therefore, in advanced metaheuristics, memory is considered as one of the fundamental elements of an efficient metaheuristic. Issues in memory usage have also been highlighted. The results of this review are beneficial to the researchers in developing efficient metaheuristics, by taking into consideration the usage of memory.
format Article
author Yasear, Shaymah Akram
Ku-Mahamud, Ku Ruhana
author_facet Yasear, Shaymah Akram
Ku-Mahamud, Ku Ruhana
author_sort Yasear, Shaymah Akram
title Taxonomy of memory usage in swarm intelligence-based metaheuristics
title_short Taxonomy of memory usage in swarm intelligence-based metaheuristics
title_full Taxonomy of memory usage in swarm intelligence-based metaheuristics
title_fullStr Taxonomy of memory usage in swarm intelligence-based metaheuristics
title_full_unstemmed Taxonomy of memory usage in swarm intelligence-based metaheuristics
title_sort taxonomy of memory usage in swarm intelligence-based metaheuristics
publisher University of Baghdad
publishDate 2019
url http://repo.uum.edu.my/27864/1/BSJ%2016%20SI%202019%20445%20452.pdf
http://repo.uum.edu.my/27864/
http://doi.org/10.21123/bsj.2019.16.2(SI).0445
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