Improving the Kepler optimization algorithm with chaotic maps: comprehensive performance evaluation and engineering applications Article Swipe
Nawal El Ghouate
,
Ahmed Bencherqui
,
Hanaa Mansouri
,
Ahmed El Maloufy
,
Mohamed Amine Tahiri
,
Hicham Karmouni
,
Mhamed Sayyouri
,
Sameh Askar
,
Mohamed Abouhawwash
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10462-024-10857-5
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10462-024-10857-5
The Kepler Optimisation Algorithm (KOA) is a recently proposed algorithm that is inspired by Kepler’s laws to predict the positions and velocities of planets at a given time. However, although promising, KOA can encounter challenges such as convergence to sub-optimal solutions or slow convergence speed. This paper proposes an improvement to KOA by integrating chaotic maps to solve complex engineering problems. The improved algorithm, named Chaotic Kepler Optimization Algorithm (CKOA), is characterized by a better ability to avoid local minima and to reach globally optimal solutions thanks to a dynamic diversification strategy based on chaotic maps. To confirm the effectiveness of the suggested approach, in-depth statistical analyses were carried out using the CEC2020 and CEC2022 benchmarks. These analyses included mean and standard deviation of fitness, convergence curves, Wilcoxon tests, as well as population diversity assessments. The experimental results, which compare CKOA not only to the original KOA but also to eight other recent optimizers, show that the proposed algorithm performs better in terms of convergence speed and solution quality. In addition, CKOA has been successfully tested on three complex engineering problems, confirming its robustness and practical effectiveness. These results make CKOA a powerful optimisation tool in a variety of complex real-world contexts. After final acceptance, the source code will be uploaded to the Github account: [email protected].
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10462-024-10857-5
- OA Status
- hybrid
- Cited By
- 18
- References
- 74
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4403089501Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10462-024-10857-5Digital Object Identifier
- Title
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Improving the Kepler optimization algorithm with chaotic maps: comprehensive performance evaluation and engineering applicationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-03Full publication date if available
- Authors
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Nawal El Ghouate, Ahmed Bencherqui, Hanaa Mansouri, Ahmed El Maloufy, Mohamed Amine Tahiri, Hicham Karmouni, Mhamed Sayyouri, Sameh Askar, Mohamed AbouhawwashList of authors in order
- Landing page
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https://doi.org/10.1007/s10462-024-10857-5Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1007/s10462-024-10857-5Direct OA link when available
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Kepler, Computer science, Chaotic, Optimization algorithm, Algorithm, Mathematical optimization, Artificial intelligence, Mathematics, Computer vision, StarsTop concepts (fields/topics) attached by OpenAlex
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18Total citation count in OpenAlex
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2025: 18Per-year citation counts (last 5 years)
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74Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.eight | 151 |
| abstract_inverted_index.final | 204 |
| abstract_inverted_index.given | 27 |
| abstract_inverted_index.local | 79 |
| abstract_inverted_index.maps. | 96 |
| abstract_inverted_index.named | 65 |
| abstract_inverted_index.other | 152 |
| abstract_inverted_index.paper | 47 |
| abstract_inverted_index.reach | 83 |
| abstract_inverted_index.solve | 58 |
| abstract_inverted_index.speed | 166 |
| abstract_inverted_index.terms | 163 |
| abstract_inverted_index.three | 178 |
| abstract_inverted_index.time. | 28 |
| abstract_inverted_index.using | 111 |
| abstract_inverted_index.which | 139 |
| abstract_inverted_index.Github | 214 |
| abstract_inverted_index.Kepler | 2, 67 |
| abstract_inverted_index.better | 75, 161 |
| abstract_inverted_index.minima | 80 |
| abstract_inverted_index.recent | 153 |
| abstract_inverted_index.source | 207 |
| abstract_inverted_index.speed. | 45 |
| abstract_inverted_index.tested | 176 |
| abstract_inverted_index.tests, | 129 |
| abstract_inverted_index.thanks | 87 |
| abstract_inverted_index.(CKOA), | 70 |
| abstract_inverted_index.CEC2020 | 113 |
| abstract_inverted_index.CEC2022 | 115 |
| abstract_inverted_index.Chaotic | 66 |
| abstract_inverted_index.ability | 76 |
| abstract_inverted_index.carried | 109 |
| abstract_inverted_index.chaotic | 55, 95 |
| abstract_inverted_index.compare | 140 |
| abstract_inverted_index.complex | 59, 179, 200 |
| abstract_inverted_index.confirm | 98 |
| abstract_inverted_index.curves, | 127 |
| abstract_inverted_index.dynamic | 90 |
| abstract_inverted_index.optimal | 85 |
| abstract_inverted_index.planets | 24 |
| abstract_inverted_index.predict | 18 |
| abstract_inverted_index.results | 189 |
| abstract_inverted_index.variety | 198 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 29 |
| abstract_inverted_index.Wilcoxon | 128 |
| abstract_inverted_index.account: | 215 |
| abstract_inverted_index.although | 30 |
| abstract_inverted_index.analyses | 107, 118 |
| abstract_inverted_index.fitness, | 125 |
| abstract_inverted_index.globally | 84 |
| abstract_inverted_index.improved | 63 |
| abstract_inverted_index.in-depth | 105 |
| abstract_inverted_index.included | 119 |
| abstract_inverted_index.inspired | 13 |
| abstract_inverted_index.original | 146 |
| abstract_inverted_index.performs | 160 |
| abstract_inverted_index.powerful | 193 |
| abstract_inverted_index.proposed | 9, 158 |
| abstract_inverted_index.proposes | 48 |
| abstract_inverted_index.quality. | 169 |
| abstract_inverted_index.recently | 8 |
| abstract_inverted_index.results, | 138 |
| abstract_inverted_index.solution | 168 |
| abstract_inverted_index.standard | 122 |
| abstract_inverted_index.strategy | 92 |
| abstract_inverted_index.uploaded | 211 |
| abstract_inverted_index.Algorithm | 4, 69 |
| abstract_inverted_index.addition, | 171 |
| abstract_inverted_index.algorithm | 10, 159 |
| abstract_inverted_index.approach, | 104 |
| abstract_inverted_index.contexts. | 202 |
| abstract_inverted_index.deviation | 123 |
| abstract_inverted_index.diversity | 134 |
| abstract_inverted_index.encounter | 34 |
| abstract_inverted_index.positions | 20 |
| abstract_inverted_index.practical | 186 |
| abstract_inverted_index.problems, | 181 |
| abstract_inverted_index.problems. | 61 |
| abstract_inverted_index.solutions | 41, 86 |
| abstract_inverted_index.suggested | 103 |
| abstract_inverted_index.Kepler’s | 15 |
| abstract_inverted_index.algorithm, | 64 |
| abstract_inverted_index.challenges | 35 |
| abstract_inverted_index.confirming | 182 |
| abstract_inverted_index.population | 133 |
| abstract_inverted_index.promising, | 31 |
| abstract_inverted_index.real-world | 201 |
| abstract_inverted_index.robustness | 184 |
| abstract_inverted_index.velocities | 22 |
| abstract_inverted_index.acceptance, | 205 |
| abstract_inverted_index.benchmarks. | 116 |
| abstract_inverted_index.convergence | 38, 44, 126, 165 |
| abstract_inverted_index.engineering | 60, 180 |
| abstract_inverted_index.improvement | 50 |
| abstract_inverted_index.integrating | 54 |
| abstract_inverted_index.optimizers, | 154 |
| abstract_inverted_index.statistical | 106 |
| abstract_inverted_index.sub-optimal | 40 |
| abstract_inverted_index.Optimisation | 3 |
| abstract_inverted_index.Optimization | 68 |
| abstract_inverted_index.assessments. | 135 |
| abstract_inverted_index.experimental | 137 |
| abstract_inverted_index.optimisation | 194 |
| abstract_inverted_index.successfully | 175 |
| abstract_inverted_index.characterized | 72 |
| abstract_inverted_index.effectiveness | 100 |
| abstract_inverted_index.effectiveness. | 187 |
| abstract_inverted_index.diversification | 91 |
| [email protected]. | 216 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.98016016 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |