A direct stochastic algorithm for global search This paper presents a new algorithm called probabilistic global search lausanne (PGSL). PGSL is founded on the assumption that optimal solutions can be identified through focusing search around sets of good solutions. Tests on benchmark problems having multi-parameter nonlinear objective functions revealed that PGSL performs better than genetic algorithms and advanced algorithms for simulated annealing in 19 out of 23 cases studied. Furthermore as problem sizes increase, PGSL performs increasingly better than these other approaches. Empirical evidence of the convergence of PGSL is provided through its application to Lennard--Jones cluster optimisation problem. Finally, PGSL has already proved to be valuable for engineering tasks in areas of design, diagnosis and control.
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