A multi-sequential number-theoretic optimization algorithm using clustering methods
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A multi-sequential number-theoretic optimization method based on clustering was developed and applied to the optimization of functions with many local extrema. Details of the procedure to generate the clusters and the sequential schedules were given. The algorithm was assessed by comparing its performance with generalized simulated annealing algorithm in a difficult instructive example and a D-optimum experimental design problem. It is shown the presented algorithm to be more effective and reliable based on the two examples.
Key wordsretention ratio cluster contraction local search
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