Solution to Advanced Manufacturing Process Problems using Cohort Intelligence Algorithm with Improved Constraint Handling Approaches Article Swipe
Aniket Nargundkar
,
Madhav Rawal
,
Aryaman Patel
,
Anand J. Kulkarni
,
Apoorva Shastri
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.10085
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.10085
Recently, various Artificial Intelligence (AI) based optimization metaheuristics are proposed and applied for a variety of problems. Cohort Intelligence (CI) algorithm is a socio inspired optimization technique which is successfully applied for solving several unconstrained & constrained real-world problems from the domains such as design, manufacturing, supply chain, healthcare, etc. Generally, real-world problems are constrained in nature. Even though most of the Evolutionary Algorithms (EAs) can efficiently solve unconstrained problems, their performance degenerates when the constraints are involved. In this paper, two novel constraint handling approaches based on modulus and hyperbolic tangent probability distributions are proposed. Constrained CI algorithm with constraint handling approaches based on triangular, modulus and hyperbolic tangent is presented and applied for optimizing advanced manufacturing processes such as Water Jet Machining (WJM), Abrasive Jet Machining (AJM), Ultrasonic Machining (USM) and Grinding process. The solutions obtained using proposed CI algorithm are compared with contemporary algorithms such as Genetic Algorithm, Simulated Annealing, Teaching Learning Based Optimization, etc. The proposed approaches achieved 2%-127% maximization of material removal rate satisfying hard constraints. As compared to the GA, CI with Hyperbolic tangent probability distribution achieved 15%, 2%, 2%, 127%, and 4% improvement in MRR for AJMB, AJMD, WJM, USM, and Grinding processes, respectively contributing to the productivity improvement. The contributions in this paper have opened several avenues for further applicability of the proposed constraint handling approaches for solving complex constrained problems.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.10085
- https://arxiv.org/pdf/2310.10085
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387724720
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4387724720Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2310.10085Digital Object Identifier
- Title
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Solution to Advanced Manufacturing Process Problems using Cohort Intelligence Algorithm with Improved Constraint Handling ApproachesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-16Full publication date if available
- Authors
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Aniket Nargundkar, Madhav Rawal, Aryaman Patel, Anand J. Kulkarni, Apoorva ShastriList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.10085Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2310.10085Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2310.10085Direct OA link when available
- Concepts
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Mathematical optimization, Algorithm, Machining, Computer science, Genetic algorithm, Simulated annealing, Optimization problem, Constraint (computer-aided design), Maximization, Mathematics, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.optimizing | 115 |
| abstract_inverted_index.processes, | 199 |
| abstract_inverted_index.real-world | 37, 51 |
| abstract_inverted_index.satisfying | 168 |
| abstract_inverted_index.Constrained | 96 |
| abstract_inverted_index.constrained | 36, 54, 227 |
| abstract_inverted_index.constraints | 75 |
| abstract_inverted_index.degenerates | 72 |
| abstract_inverted_index.efficiently | 66 |
| abstract_inverted_index.healthcare, | 48 |
| abstract_inverted_index.improvement | 189 |
| abstract_inverted_index.performance | 71 |
| abstract_inverted_index.probability | 92, 180 |
| abstract_inverted_index.triangular, | 105 |
| abstract_inverted_index.Evolutionary | 62 |
| abstract_inverted_index.Intelligence | 3, 18 |
| abstract_inverted_index.constraints. | 170 |
| abstract_inverted_index.contemporary | 145 |
| abstract_inverted_index.contributing | 201 |
| abstract_inverted_index.distribution | 181 |
| abstract_inverted_index.improvement. | 205 |
| abstract_inverted_index.maximization | 163 |
| abstract_inverted_index.optimization | 6, 25 |
| abstract_inverted_index.productivity | 204 |
| abstract_inverted_index.respectively | 200 |
| abstract_inverted_index.successfully | 29 |
| abstract_inverted_index.Optimization, | 156 |
| abstract_inverted_index.applicability | 217 |
| abstract_inverted_index.contributions | 207 |
| abstract_inverted_index.distributions | 93 |
| abstract_inverted_index.manufacturing | 117 |
| abstract_inverted_index.unconstrained | 34, 68 |
| abstract_inverted_index.manufacturing, | 45 |
| abstract_inverted_index.metaheuristics | 7 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |