A Metropolis algorithm combined with Hooke-Jeeves local search method applied to global optimization

Abstract A hybridization of a recently introduced Metropolis algorithm named the Particle Collision Algorithm (PCA) and the Hooke–Jeeves local search method is applied to a testbed of global optimization functions and to real-world chemical equilibrium nonlinear systems. The results obtained by this method, called HJPCA, are compared against those achieved by two state-of-the-art global optimization methods, C-GRASP and GLOBAL. HJPCA performs better than both algorithms, thus demonstrating its potential for other applications.

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