MORIME: A multi-objective RIME optimization framework for efficient truss design Article Swipe
Mohammad Aljaidi
,
Nikunj Mashru
,
Pinank Patel
,
Divya Adalja
,
Pradeep Jangir
,
Arpita Arpita
,
Sundaram B. Pandya
,
Mohammad Khishe
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2025.103933
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2025.103933
Multi objective optimization (MOO) is very important in structural engineering, especially in truss design where a trade off between weight reduction and compliance is needed to maximize the efficiency. Usually, conventional optimization algorithms have difficulty in solving complex MOO tasks, and in generating diverse, high quality solutions for various structural configurations. To overcome these challenges, this study proposes a Multi Objective RIME (MORIME) algorithm that uses improved non-dominated sorting and crowding distance techniques to optimize weight and compliance over eight truss designs. With respect to Hypervolume (HV), Inverted Generational Distance (IGD), and Spacing (SP) metrics, it performed better than other established methods such as NSGA-II, MOEA/D, MOMVO, MOTEO, MOLCA, and MORIME, leading to better convergence and diversity of solution sets. The results show that MORIME is a good tool for dealing with complex multi objective optimization landscapes and that it is better than biologically inspired and hybrid optimization methods. MORIME is a powerful tool for structural engineers to produce well balanced truss designs, which meet stringent weight and compliance requirements in a multiobjective setting. MORIME is one attractive feature because it can generate optimal and diverse solutions in truss optimization, resulting in high quality design results.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.rineng.2025.103933
- OA Status
- gold
- Cited By
- 51
- References
- 41
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4406080106Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.rineng.2025.103933Digital Object Identifier
- Title
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MORIME: A multi-objective RIME optimization framework for efficient truss designWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-01-05Full publication date if available
- Authors
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Mohammad Aljaidi, Nikunj Mashru, Pinank Patel, Divya Adalja, Pradeep Jangir, Arpita Arpita, Sundaram B. Pandya, Mohammad KhisheList of authors in order
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https://doi.org/10.1016/j.rineng.2025.103933Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.rineng.2025.103933Direct OA link when available
- Concepts
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Hard rime, Truss, Computer science, Mathematical optimization, Engineering, Mathematics, Structural engineering, Art, LiteratureTop concepts (fields/topics) attached by OpenAlex
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51Total citation count in OpenAlex
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2025: 51Per-year citation counts (last 5 years)
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| abstract_inverted_index.sorting | 68 |
| abstract_inverted_index.various | 48 |
| abstract_inverted_index.(MORIME) | 62 |
| abstract_inverted_index.Distance | 89 |
| abstract_inverted_index.Inverted | 87 |
| abstract_inverted_index.NSGA-II, | 104 |
| abstract_inverted_index.Usually, | 29 |
| abstract_inverted_index.balanced | 160 |
| abstract_inverted_index.crowding | 70 |
| abstract_inverted_index.designs, | 162 |
| abstract_inverted_index.designs. | 81 |
| abstract_inverted_index.distance | 71 |
| abstract_inverted_index.diverse, | 43 |
| abstract_inverted_index.generate | 182 |
| abstract_inverted_index.improved | 66 |
| abstract_inverted_index.inspired | 144 |
| abstract_inverted_index.maximize | 26 |
| abstract_inverted_index.methods. | 148 |
| abstract_inverted_index.metrics, | 94 |
| abstract_inverted_index.optimize | 74 |
| abstract_inverted_index.overcome | 52 |
| abstract_inverted_index.powerful | 152 |
| abstract_inverted_index.proposes | 57 |
| abstract_inverted_index.results. | 195 |
| abstract_inverted_index.setting. | 173 |
| abstract_inverted_index.solution | 118 |
| abstract_inverted_index.Objective | 60 |
| abstract_inverted_index.algorithm | 63 |
| abstract_inverted_index.diversity | 116 |
| abstract_inverted_index.engineers | 156 |
| abstract_inverted_index.important | 6 |
| abstract_inverted_index.objective | 1, 134 |
| abstract_inverted_index.performed | 96 |
| abstract_inverted_index.reduction | 20 |
| abstract_inverted_index.resulting | 190 |
| abstract_inverted_index.solutions | 46, 186 |
| abstract_inverted_index.stringent | 165 |
| abstract_inverted_index.algorithms | 32 |
| abstract_inverted_index.attractive | 177 |
| abstract_inverted_index.compliance | 22, 77, 168 |
| abstract_inverted_index.difficulty | 34 |
| abstract_inverted_index.especially | 10 |
| abstract_inverted_index.generating | 42 |
| abstract_inverted_index.landscapes | 136 |
| abstract_inverted_index.structural | 8, 49, 155 |
| abstract_inverted_index.techniques | 72 |
| abstract_inverted_index.Hypervolume | 85 |
| abstract_inverted_index.challenges, | 54 |
| abstract_inverted_index.convergence | 114 |
| abstract_inverted_index.efficiency. | 28 |
| abstract_inverted_index.established | 100 |
| abstract_inverted_index.Generational | 88 |
| abstract_inverted_index.biologically | 143 |
| abstract_inverted_index.conventional | 30 |
| abstract_inverted_index.engineering, | 9 |
| abstract_inverted_index.optimization | 2, 31, 135, 147 |
| abstract_inverted_index.requirements | 169 |
| abstract_inverted_index.non-dominated | 67 |
| abstract_inverted_index.optimization, | 189 |
| abstract_inverted_index.multiobjective | 172 |
| abstract_inverted_index.configurations. | 50 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.99984435 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |