MOTEO: A novel physics-based multiobjective thermal exchange optimization algorithm to design truss structures

The present study investigates a novel Multiobjective Thermal Exchange Optimization (MOTEO) algorithm for truss design. Established on Newton’s law of cooling framework, this multiobjective version is revised and further improved from the single-objective version of Thermal Exchange Optimization usi...

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Vydáno v:Knowledge-based systems Ročník 242; s. 108422
Hlavní autoři: Kumar, Sumit, Jangir, Pradeep, Tejani, Ghanshyam G., Premkumar, Manoharan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 22.04.2022
Elsevier Science Ltd
Témata:
Algorithms
Bars
Convergence
Crowding
Crowding stress
Design optimization
Exchanging
Multiobjective problems, Physics-based algorithm
Multiple objective analysis
Newton Theory
Novels
Optimization
Pareto front
Performance evaluation
Performance indicators
Physics
Quantitative analysis
Reaction time
Response time
Statistical analysis
Structural optimization, Metaheuristics
Trusses
Structural optimization, Metaheuristics
Pareto front
Multiobjective problems, Physics-based algorithm
ISSN:0950-7051, 1872-7409
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Shrnutí:The present study investigates a novel Multiobjective Thermal Exchange Optimization (MOTEO) algorithm for truss design. Established on Newton’s law of cooling framework, this multiobjective version is revised and further improved from the single-objective version of Thermal Exchange Optimization using the nondominated sorting and crowding distancing methods. To evaluate the performance, eight structural optimization problems and five ZDT benchmark problems were examined, and the outcomes were contrasted with four state-of-the-art optimization methodologies. Minimizing the truss’s mass and maximizing nodal deflection are the two conflicting objectives considered subject to stress constraints for the 10-bar, 25-bar, 60-bar ring, 72-bar, 120-bar, 200-bar, and 942-bar truss problems. The statistical analysis is conducted on ten performance indicators results and obtained the best Pareto Fronts comparison. The findings revealed that MOTEO finds the best solutions with a shorter response time and has improved convergence, diversity, and spread behavior across Pareto Fronts.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2022.108422