Hybrid Harmony Search Optimization Algorithm for Continuous Functions

This paper proposes a hybrid harmony search algorithm that incorporates method of reinitializing harmonies memory using particle swarm optimization with an improved opposition-based learning (IOBL) to solve continuous problems. allows the obtain better results by increasing space solutions. approach has been validated comparing performance proposed state-of-the-art algorithm, solving fifteen standard mathematical functions, and applying Wilcoxon parametric test at 5% significance level. The uses improvement (IOBL). Computational experiments show outperforms algorithm. In quality, it is in fourteen instances, efficiency seven instances.