An improved global-best harmony search algorithm for faster optimization

•A new initialization is employed based on opposition-based learning.•A hybrid scheme for improvising a new harmony.•A dynamically updating scheme for parameters adjustment.•Two perturbed schemes for the best harmony. In this paper, an improved global-best harmony search algorithm, named IGHS, is pr...

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Bibliographic Details
Published in:Expert systems with applications 2014-10, Vol.41 (13), p.5788-5803
Main Authors: Xiang, Wan-li, An, Mei-qing, Li, Yin-zhen, He, Rui-chun, Zhang, Jing-fang
Format: Article
Language:English
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Approximation
Artificial intelligence
Computer science
control theory
systems
Convergence
Evolution
Exact sciences and technology
Expert systems
Exploration and exploitation
Global-best harmony search
Harmony search
Numerical optimization
Opposition-based learning
Periodic and sign function
Search algorithms
Searching
Swarm intelligence
Theoretical computing
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Summary:•A new initialization is employed based on opposition-based learning.•A hybrid scheme for improvising a new harmony.•A dynamically updating scheme for parameters adjustment.•Two perturbed schemes for the best harmony. In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2014.03.016