An opposition-based harmony search algorithm for engineering optimization problems

Harmony search (HS) is a derivative-free real parameter optimization algorithm. It draws inspiration from the musical improvisation process of searching for a perfect state of harmony. The proposed opposition-based HS (OHS) of the present work employs opposition-based learning for harmony memory initialization and also for generation jumping. The concept of opposite number is utilized in OHS to improve the convergence rate of the HS algorithm. The potential of the proposed algorithm is assessed by means of an extensive comparative study of the numerical results on sixteen benchmark test functions. Additionally, the effectiveness of the proposed algorithm is tested for reactive power compensation of an autonomous power system. For real-time reactive power compensation of the studied model, Takagi Sugeno fuzzy logic (TSFL) is employed. Time-domain simulation reveals that the proposed OHS-TSFL yields on-line, off-nominal model parameters, resulting in real-time incremental change in terminal voltage response profile.