Topology optimization of truss subjected to static and dynamic constraints by integrating simulated annealing into passing vehicle search algorithms

Three modified versions of passing vehicle search (PVS) are proposed and tested on truss topology optimization with static and dynamic constraints. PVS works on the mechanism of passing a vehicle on a two-lane highway. The heuristic nature of PVS allows the search to jump into non-visited regions (e...

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Vydáno v:Engineering with computers Ročník 35; číslo 2; s. 499 - 517
Hlavní autoři: Tejani, Ghanshyam G., Savsani, Vimal J., Bureerat, Sujin, Patel, Vivek K., Savsani, Poonam
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.04.2019
Springer Nature B.V
Témata:
Algorithms
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Computer Science
Computer simulation
Computer-Aided Engineering (CAD
Constraints
Control
Math. Applications in Chemistry
Mathematical and Computational Engineering
Original Article
Search algorithms
Simulated annealing
Systems Theory
Topology optimization
Modified algorithm
Parallel computing
Metaheuristic
Structural optimization
Frequency
Static and dynamic constraints
Buckling
ISSN:0177-0667, 1435-5663
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Popis
Shrnutí:Three modified versions of passing vehicle search (PVS) are proposed and tested on truss topology optimization with static and dynamic constraints. PVS works on the mechanism of passing a vehicle on a two-lane highway. The heuristic nature of PVS allows the search to jump into non-visited regions (exploration) and also permits a local search of visited regions (exploitation). First, the original PVS algorithm is improved to avoid a local optima trap using a novel parallel run mechanism. Then, population diversity is improved by incorporating the selection of simulated annealing. The various versions of PVS are verified on the truss design problems. Comparative results show that the parallel run concept improves the original PVS algorithm. The selection using the Boltzmann probability as used in simulated annealing further improves the algorithm.
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-018-0612-8