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Abstract
A multistart, step-controlled random search algorithm for global, constrained optimization is proposed. The method is found to be very efficient for solving a variety of constrained nonlinear optimization problems. The performance of the method and its comparison with another stochastic search algorithm, simulating annealing, are demonstrated through a number of standard test problems involving multimodal objective functions with continuous and mixed-discrete variables. The applications of the method to a number of practical engineering optimization cases in the field of turbine design and compact heat exchangers are discussed.