Performance Of Harmony Search Research Articles

Research on the performance of harmony search with local search algorithms for solving flexible job-shop scheduling problem

Combining the harmony search algorithm (HS) with the local search algorithm (LS) can prevent the HS from falling into a local optimum. However, how LS affects the performance of HS has not yet been studied systematically. Therefore, in this paper, it is first proposed to combine four frequently used LS with HS to obtain several search algorithms (HSLSs). Then, by taking the flexible job-shop scheduling problem (FJSP) as an example and considering decoding times, study how the parameters of HSLSs affect their performance, where the performance is evaluated by the difference rate based on the decoding times. The simulation results mainly show that (I) as the harmony memory size (HMS) gradually increases, the performance of HSLSs first increases rapidly and then tends to remain unchanged, and HMS is not the larger the better; (II) as harmony memory considering rate increases, the performance continues to improve, while the performance of pitch adjusting rate on HSLSs goes to the opposite; Finally, more benchmark instances are also used to verify the effectiveness of the proposed algorithms. The results of this paper have a certain guiding significance on how to choose LS and other parameters to improve HS for solving FJSP.

Improved Global Harmony Search Algorithm for Numerical Optimization

To intend to improve the optimization performance of harmony search (HS) algorithm, an improved global harmony search (IGHS) algorithm was presented in this paper. In this algorithm, inspired by swarm intelligence, the global best harmony are borrowed to enhance the optimization accuracy of HS; and mutation and crossover operation instead of pitch adjustment operation to improved the algorithm convergence rate. The key parameters are adjusted to balance the local and global search. Several benchmark experiment simulations, the IGHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its other three improved algorithms (IHS, GHS and SGHS) that reported in recent literature.

Improving the performance of harmony search using opposition-based learning and quadratic interpolation

Harmony search (HS) is a recently spotlighted metaheuristic optimisation method imitating the music improvisation process where musicians improvise their instruments' pitches searching for a perfect state of harmony. In this paper, the effect of using opposition-based learning and quadratic interpolation is investigated. Three variants are proposed and the performance of these methods is investigated and compared with HS and other recent methods when applied to several benchmark functions. The experiments conducted show that the proposed methods generally outperformed the other methods when applied to the benchmark problems. Moreover, the performance of the proposed methods when applied to high-dimensional problems is investigated.