A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems
<p>Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are m...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , |
| Published: |
2021
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513563261927424 |
|---|---|
| author | Wali Khan Mashwani (14590504) |
| author2 | Ruqayya Haider (14590505) Samir Brahim Belhaouari (11277910) |
| author2_role | author author |
| author_facet | Wali Khan Mashwani (14590504) Ruqayya Haider (14590505) Samir Brahim Belhaouari (11277910) |
| author_role | author |
| dc.creator.none.fl_str_mv | Wali Khan Mashwani (14590504) Ruqayya Haider (14590505) Samir Brahim Belhaouari (11277910) |
| dc.date.none.fl_str_mv | 2021-02-28T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1155/2021/5521951 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Multiswarm_Intelligence_Algorithm_for_Expensive_Bound_Constrained_Optimization_Problems/22058741 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science applications evolutionary algorithms evolutionary computation computer science |
| dc.title.none.fl_str_mv | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems. </p> <h2>Other information</h2> <p>Published in: Complexity<br> License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br> See article on publisher's website: <a href="http://dx.doi.org/10.1155/2021/5521951" target="_blank">http://dx.doi.org/10.1155/2021/5521951</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_75bcf6dbb93c44cafa4c59fd0e5638a8 |
| identifier_str_mv | 10.1155/2021/5521951 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/22058741 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization ProblemsWali Khan Mashwani (14590504)Ruqayya Haider (14590505)Samir Brahim Belhaouari (11277910)Information and computing sciencesArtificial intelligenceData management and data scienceapplicationsevolutionary algorithmsevolutionary computationcomputer science<p>Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems. </p> <h2>Other information</h2> <p>Published in: Complexity<br> License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br> See article on publisher's website: <a href="http://dx.doi.org/10.1155/2021/5521951" target="_blank">http://dx.doi.org/10.1155/2021/5521951</a></p>2021-02-28T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1155/2021/5521951https://figshare.com/articles/journal_contribution/A_Multiswarm_Intelligence_Algorithm_for_Expensive_Bound_Constrained_Optimization_Problems/22058741CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/220587412021-02-28T06:00:00Z |
| spellingShingle | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems Wali Khan Mashwani (14590504) Information and computing sciences Artificial intelligence Data management and data science applications evolutionary algorithms evolutionary computation computer science |
| status_str | publishedVersion |
| title | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| title_full | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| title_fullStr | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| title_full_unstemmed | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| title_short | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| title_sort | A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems |
| topic | Information and computing sciences Artificial intelligence Data management and data science applications evolutionary algorithms evolutionary computation computer science |