Modified Tournament Harmony Search for Unconstrained Optimisation Problems

Lately, Harmony Search algorithm (HSA) has attracted the attentions of researchers in operation research and artificial intelligence domain due to its capabilities of solving complex optimization problems in various fields. Different variants of HSA were proposed to overcome its weaknesses such as stagnation at local optima and slow convergence. The limitations of HSA have been mainly addressed in three aspects: studying the effect of HSA parameter settings, hybridizing it with other part of metaheuristic algorithms and the selection schemes that are used in selecting decision variables from harmony memory vectors. This paper focuses on improving the performance of HSA by introducing a new variant of HSA named Modified Tournament Harmony Search (MTHS) algorithm. The MTHS modifies the tournament selection scheme in order to improve the performance and efficiency of the classical HSA. Empirical results demonstrate the effectiveness of the proposed MTHS method and show its significance when compared with three benchmark variants of HSA.