A quantitative analysis of the simulated annealing algorithm: A case study for the traveling salesman problem

Traveling Transportation Problem Optimization by Adaptive Current Search Method

“…at least one pair of algorithms (9) As results of solution quality in Table 11 -Table 12, it was found that for all algorithms, there is significant difference (p-value < 0.05) among those algorithms. ACS outperforms GA on all four TTP problems (BN, CT, NT and NET), TS on three TTP problems (CT, NT and NET) and CS on one TTP problem (NET).…”

“…However, the optimum solution could not be guaranteed. Up to date, the metaheuristic optimization techniques have been applied to solve the TPS and TTP problems, for example simulated annealing (SA) [9], artificial neural network (ANN) [10], tabu search (TS) [11], genetic algorithms (GA) [12], scatter search [13], particle swarm optimization [14], ant colony optimization [15] and adaptive tabu search (ATS) [16].…”

Molecular structure matching by simulated annealing. I. A comparison between different cooling schedules

“…The value of the constant used in Eq. 9 for scaling AE affects the acceptance probability for transitions involving uphill excursions of E. Tests were performed with the Aarts et al [12] schedule with an initial temperature set to T = 2 to determine an appropriate constant.…”

“…Simulated annealing is a general technique that can be used to minimize any problem in a discrete space. It has been successfully applied to: the travelling salesman problem (TSP) [11,12], the placement of standard cells in circuit design [13], the minimization of the Hamiltonian for flips of spins in spin glasses [11,14], the quadratic assignment problem [15,16], the football pools [17] and crystallographic refinement of proteins [18,19]. A considerable number of algorithms exist for simulated annealing and they differ in important respects; the principal differences are how they handle the temperature decrement, the length of the Markov chains and the stop criterion.…”

“…where < E > Ti is the mean value of E at a particular Ti, aE is the standard deviation of E at T i and 6 is a small positive number, set here to 6 = 0.02 [12].…”