A particle swarm minimization algorithm with enhanced hill climbing capability
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We propose a particle swarm minimization algorithm with enhanced hill climbing capability. In the algorithm, an inferior solution is accepted as a new local best if the current cost function value is lower than that of the previous iteration. Numerical results are presented for a popular test set and two practical global optimization problems, which illustrate that the proposed algorithm may outperform the classical particle swarm algorithm for certain classes of problems.