A novel hybrid wolf pack algorithm with harmony search for global numerical optimization
Xiayang Chen, Chaojing Tang, Jian Wang, Lei Zhang · 2017
Though the wolf pack algorithm (WPA) is a new optimal algorithm with good performance in optimation, its convergence is slow. Aiming at speeding up the convergence of the wolf pack algorithm (WPA), a novel hybrid wolf pack algorithm with harmony search (HS) was proposed, which named WPAHS for short. Harmony search is introduced into the process of the wolves updating to substitute the random update. With the help of the employment of harmony memory, pitch adjusting, and randomization mechanisms of HS, the WPAHS not only obtain a faster convergence, but also keeps a feasible adaptability for a wider range of optimation. Six standard benchmark functions are applied to verify the effects of these improvements. It is shown that the performance of WPAHS is superior to, or at least highly competitive with, the standard WPA and other swarm intelligence optimization algorithm, such as GA, HS, PSO, and ABC in most situations.