Opposition-based learning in global harmony search algorithm

This paper proposes an opposition-based learning global harmony search (OLGHS) algorithm. An oppositionbased learning initialization technique is employed for initialize the harmony memory to enhance the quality of the initial harmony vector. The worst harmony learns from the best harmony, which can improve the global search performance of the algorithm. The local search performance of the algorithm is enhanced by means of random learning strategy of backtracking interaction among other harmony vectors. The new harmony is dynamically generated by means of random global crossover with two different learning strategies, and the harmony memory is updated by the optimal individual of the improvising harmony and its opposition harmony. Finally, a comparison test with other heuristic optimization algorithms and HS variants is carried out to test the optimization performance of the proposed algorithm. The simulation results demonstrate the OLGHS algorithm has higher convergence precision and convergence rate. © 2019, Editorial Office of Control and Decision. All right reserved.