A Hybrid Harmony search and Simulated Annealing algorithm for continuous optimization

Harmony search is a powerful metaheuristic algorithm with excellent exploitation capabilities but suffers a very serious limitation of premature convergence if one or more initially generated solutions/harmonies are in the vicinity of local optimal. In order to remove this limitation this paper proposes a novel algorithm based on hybridization of Harmony search and Simulated Annealing called HS-SA to inherit their advantages in a complementary way. Taking the inspiration from Simulated Annealing the proposed HS-SA algorithm accepts even the inferior harmonies with a probability determined by parameter called Temperature. The Temperature parameter is initially kept high to favor exploration of search space and is linearly decreased to gradually shift focus to exploitation of promising search areas. The performance of HS-SA is tested on IEEE CEC 2014 benchmark functions and real life problem from computer vision called Camera Calibration problem. The numerical results demonstrate the superiority of the proposed algorithm.