A hybrid swarm intelligence algorithm for multiuser scheduling in HSDPA
Applied Soft Computing
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This thesis describes a new, efficient, and general purpose parallel simulated annealing algorithm. The algorithm is based on periodic mixing steps, in which favorable states reproduce and unfavor able ones are destroyed. It runs on a distributed memory Multiple Instructions Multiple Data architecture parallel computer. Parallel efficiency is controlled by the interval between mixing steps. In this thesis, it is shown that for certain values of this interval found by exhaustive search, the algorithm can give up to 100% parallel efficiency on up to 50 processors and 80% parallel efficiency on 100 processors. Moreover, for a given number of processors, there is a range of mixing interval which gives high parallel efficiency. In this thesis, two efficient statistical estimators, namely, the cross-correlation and variance among processors are defined for finding efficient mixing intervals are constructed which give parallel efficiency of 75% without exhaustive search. This is done by tracking the two statistical estimators right after communication, so as to obtain the lower and upper bounds for the optimal mixing interval. The algorithm was tested on two problems. One was an inverse problem on gene expression dynamics in developmental biology, and the other was the Lennard-Jones potential problem.