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

• Published in: Knowledge-based systems Vol. 242; p. 108422
• Main Authors: Kumar, Sumit, Jangir, Pradeep, Tejani, Ghanshyam G., Premkumar, Manoharan
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 22.04.2022; Elsevier Science Ltd
• Subjects: Algorithms; Design optimization; Exchanging; Multiobjective problems, Physics-based algorithm; Multiple objective analysis; Newton Theory; Optimization; Pareto front; Performance evaluation; Response time; Statistical analysis; Structural optimization, Metaheuristics; Trusses; Structural optimization, Metaheuristics; Pareto front; Multiobjective problems, Physics-based algorithm
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2022.108422

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.