Abstract
In this paper we present a generalization of the sequential simulated annealing algorithm for combinatorial optimization problems. By performing a parallel study of the current solution neighbourhood we obtain an algorithm that can be very efficiently implemented on a massively parallel computer. We test the convergence and the quality of our algorithm by comparing it to the sequential algorithm for two classical problems: the minimization of an unconstrained 0–1 quadratic function and the quadratic sum assignment problem.
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