A Modified Global Harmony Search Algorithm with Random Crossover for Continuous High Dimensional Optimization Problems

This paper proposes a modified global harmony search (MGHS) algorithm with random crossover algorithm to solve continuous high dimensional optimization problems. For the problem of premature convergence in harmony search algorithm, in the improvisation stage of MGHS algorithm, the new harmony vector is generated dynamically by means of random crossover for the global optimization problems, i.e., the worst harmony learning from the best harmony and the random selected other harmony learning from the best harmony random crossover strategy. Finally, MGHS algorithm is applied in the simulation test of 8 benchmark functions, the simulation results demonstrate the MGHS algorithm has higher convergence precision and convergence rate.