Abstract
A novel global harmony search (NGHS) algorithm, as proposed in 2010, is an improved algorithm that combines the harmony search (HS), particle swarm optimization (PSO), and a genetic algorithm (GA). Moreover, the fixed parameter of mutation probability was used in the NGHS algorithm. However, appropriate parameters can enhance the searching ability of a metaheuristic algorithm, and their importance has been described in many studies. Inspired by the adjustment strategy of the improved harmony search (IHS) algorithm, a dynamic adjusting novel global harmony search (DANGHS) algorithm, which combines NGHS and dynamic adjustment strategies for genetic mutation probability, is introduced in this paper. Moreover, extensive computational experiments and comparisons are carried out for 14 benchmark continuous optimization problems. The results show that the proposed DANGHS algorithm has better performance in comparison with other HS algorithms in most problems. In addition, the proposed algorithm is more efficient than previous methods. Finally, different strategies are suitable for different situations. Among these strategies, the most interesting and exciting strategy is the periodic dynamic adjustment strategy. For a specific problem, the periodic dynamic adjustment strategy could have better performance in comparison with other decreasing or increasing strategies. These results inspire us to further investigate this kind of periodic dynamic adjustment strategy in future experiments.
Highlights
The last two decades have seen a significant increase in research into metaheuristic algorithms.The procedure of a metaheuristic algorithm can be divided into four steps: initialization, movement, replacement, and iteration [1]
A dynamic adjusting novel global harmony search (DANGHS) algorithm was proposed in this paper
HS is similar in concept to other metaheuristic algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) in terms of combining the rules of randomness to imitate the process that inspired it
Summary
The last two decades have seen a significant increase in research into metaheuristic algorithms. The HS algorithm suffers a very serious limitation of premature exploitation, is the ability of an algorithm to exploit the search space in the vicinity of the current good convergence if one or more initially generated harmonies are in the vicinity of local optimal [21]. New regions of a large search space and allows dissemination of the new information into the in order to eradicate the aforementioned limitation, several improved HS algorithms population Proper balance between these two contradicting characteristics is a must to enhance the have been proposed, such as the improved harmony search (IHS) algorithm [23], the self-adaptive performance of the algorithm.”. A dynamic adjusting novel global harmony search (DANGHS) algorithm was proposed in this paper. The HS, the IHS, the SGHS, and the NGHS are reviewed
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