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

2018 ◽  
Vol 35 (2) ◽  
pp. 499-517 ◽  
Author(s):  
Ghanshyam G. Tejani ◽  
Vimal J. Savsani ◽  
Sujin Bureerat ◽  
Vivek K. Patel ◽  
Poonam Savsani
Keyword(s):  
Simulated Annealing ◽  
Topology Optimization ◽  
Search Algorithms ◽  
Dynamic Constraints

scholarly journals Combining Interval Branch and Bound and Stochastic Search

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Dhiranuch Bunnag
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Branch And Bound ◽  
Lower Cost ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Constrained Problems ◽  
Interval Branch And Bound ◽  
Unconstrained Problems

This paper presents global optimization algorithms that incorporate the idea of an interval branch and bound and the stochastic search algorithms. Two algorithms for unconstrained problems are proposed, the hybrid interval simulated annealing and the combined interval branch and bound and genetic algorithm. The numerical experiment shows better results compared to Hansen’s algorithm and simulated annealing in terms of the storage, speed, and number of function evaluations. The convergence proof is described. Moreover, the idea of both algorithms suggests a structure for an integrated interval branch and bound and genetic algorithm for constrained problems in which the algorithm is described and tested. The aim is to capture one of the solutions with higher accuracy and lower cost. The results show better quality of the solutions with less number of function evaluations compared with the traditional GA.

SIMULATED ANNEALING AND TABU SEARCH ALGORITHMS FOR MULTIWAY GRAPH PARTITION

1992 ◽  
Vol 02 (02) ◽  
pp. 159-185 ◽  
Author(s):  
L. TAO ◽  
Y.C. ZHAO ◽  
K. THULASIRAMAN ◽  
M.N.S. SWAMY
Keyword(s):  
Experimental Study ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Time Frame ◽  
Search Algorithms ◽  
Graph Partition ◽  
Solution Quality ◽  
Running Time ◽  
New Algorithms

For a given graph G with vertex and edge weights, we partition the vertices into subsets to minimize the total weights for edges crossing the subsets (weighted cut size) under the constraint that the vertex weights are evenly distributed among the subsets. We propose two new effective graph partition algorithms based on simulated annealing and tabu search, and compare their performance with that of the LPK algorithm reported in Ref. 12. Extensive experimental study shows that both of our new algorithms produce significantly better solutions than the LPK algorithm (maximal and minimal improvements on average weighted cut size are roughly 51.8% and 10.5% respectively) with longer running time, and this advantage in solution quality would not change even if we run the LPK algorithm repeatedly with random initial solutions in the same time frame as required by our algorithms.

scholarly journals Post-Processing of non gradient-based Topology Optimization with Simulated Annealing

2021 ◽  
Vol 54 (1) ◽  
pp. 755-760
Author(s):  
Hossein R. Najafabadi ◽  
Tiago Goto ◽  
Mizael Falheiro ◽  
Thiago C. Martins ◽  
Ahmad Barari ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Topology Optimization ◽  
Post Processing ◽  
Gradient Based
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