A harmony search (HS) algorithm is based on musical performance processes that occur when a musician searches for a better state of harmony. Harmony search has successfully been applied to a wide variety of practical optimization problems. The improved harmony search (IHS) dynamically updates adjusting bandwidth (bw) and pitch adjusting rate (PAR). A new variant of HS, called the enhancement of improved harmony search (EIHS) is proposed in this paper, where the key difference between this algorithm and IHS method is in the way of calculating bw and PAR. PAR and bw are a very important factor for the high efficiency of the harmony search algorithms and can be potentially useful in adjusting convergence rate of algorithms to optimal solution. Social networking optimization problems are presented to demonstrate the effectiveness and robustness of these algorithms. In all cases, the solutions obtained using EIHS are in agreement or better than those obtained from other methods. Finally, the experimental results of traditional HS, IHS, and EIHS for optimization social network problems in different iterations are visualized to illustrate the performance of each algorithm. And visualization for fitness function of the enhancement of improved harmony search is proposed in this paper.

harmony search, adjusting bandwidth, pitch adjusting rate, optimization problem, social network, visualization.