Abstract
Harmony search algorithm is a meta-heuristic optimization method imitating the music improvisation process, where musicians improvise their instruments’ pitches searching for a perfect state of harmony. First, an improved harmony search algorithm is presented using the concept of swarm intelligence. Next, it is employed for training feedforward neural networks for three benchmark classification problems. Then, the performance of the proposed algorithm is compared with that of three methods. Simulation results demonstrate the effectiveness of the proposed algorithm.
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