For the purpose of avoiding the disadvantage of harmony search algorithm, a learned harmony search(LHS)
algorithm is proposed. The adaptive parameter harmony memory consideration rate(HMCR) is designed based on the change
of objective function value and the learning strategy is used to accelerate the speed of search. Then pitch adjustment rate(PAR)
is adjusted dynamically to enhance the global search. The 16 classic test functions are tested, and the results show that LHS
algorithm outperforms the other four harmony search algorithms. Finally, LHS algorithm is applied to 10 0-1 knapsack
problems and a classic knapsack example, and the result shows that LHS algorithm is better than other algorithms.