Multi-modal Battle Royale optimizer Article Swipe
K. Dilşad Çiçek
,
Taymaz Akan
,
Oğuz Bayat
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10586-024-04399-2
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10586-024-04399-2
Multimodal optimization poses a challenging problem in the field of optimization as it entails the discovery of multiple local and global optima, unlike unimodal optimization, which seeks a single global solution. In recent years, the significance of addressing multimodal optimization challenges has grown due to the real-world complexity of many problems. While numerous optimization methods are available for unimodal problems, multimodal optimization techniques have garnered increased attention. However, these approaches often grapple with a common issue: the determination of the niching parameter, necessitating prior knowledge of the problem space. This paper introduces a novel multimodal optimization approach that circumvents the need for prior problem space knowledge and avoids the challenge of predefining the niching parameter. Building upon the Battle Royal Optimization (BRO) algorithm, this extended version formulates a multimodal solution by utilizing Coulomb's law to identify suitable neighbors. The incorporation of Coulomb's law serves the dual purpose of identifying potential local and global optima based on fitness values and establishing optimal distances from solution candidates. A comparison study was done between the MBRO and seven well-known multimodal optimization algorithms using 14 benchmark problems from the CEC 2013 and CEC 2015 competitions to see how well it worked. The experimental results underscore MBRO's proficiency in successfully identifying most, if not all, local and global optima, positioning it as a superior solution when compared to its competitors.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10586-024-04399-2
- https://link.springer.com/content/pdf/10.1007/s10586-024-04399-2.pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394792405
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4394792405Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s10586-024-04399-2Digital Object Identifier
- Title
-
Multi-modal Battle Royale optimizerWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-14Full publication date if available
- Authors
-
K. Dilşad Çiçek, Taymaz Akan, Oğuz BayatList of authors in order
- Landing page
-
https://doi.org/10.1007/s10586-024-04399-2Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10586-024-04399-2.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s10586-024-04399-2.pdfDirect OA link when available
- Concepts
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Computer science, Local optimum, Mathematical optimization, Benchmark (surveying), Global optimization, Optimization problem, Multi-objective optimization, Field (mathematics), Artificial intelligence, Machine learning, Algorithm, Mathematics, Pure mathematics, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5083608225 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I81020160 |
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| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.83199933 |
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| citation_normalized_percentile.is_in_top_10_percent | False |