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An intelligent global harmony search approach to continuous optimization problems

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  • Valian, Ehsan
  • Tavakoli, Saeed
  • Mohanna, Shahram

Abstract

Harmony search algorithm is a meta-heuristic optimization method imitating the music improvisation process, where musicians improvise their instruments’ pitches searching for a perfect state of harmony. To solve continuous optimization problems more efficiently, this paper presents an improved harmony search algorithm using the swarm intelligence technique. Applying the proposed algorithm to several well-known benchmark problems, it is shown that it can find better solutions in comparison with both basic harmony search algorithms, and improved harmony search algorithms such as the self-adaptive global-best harmony search as well as novel global harmony search. Furthermore, a study on the effect of changing the parameters of the proposed algorithm on its performance is carried out. Finally, the proper values of the algorithm parameters are suggested.

Suggested Citation

  • Valian, Ehsan & Tavakoli, Saeed & Mohanna, Shahram, 2014. "An intelligent global harmony search approach to continuous optimization problems," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 670-684.
    • Handle: RePEc:eee:apmaco:v:232:y:2014:i:c:p:670-684
    DOI: 10.1016/j.amc.2014.01.086
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    References listed on IDEAS

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    1. Ceylan, Huseyin & Ceylan, Halim & Haldenbilen, Soner & Baskan, Ozgur, 2008. "Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey," Energy Policy, Elsevier, vol. 36(7), pages 2527-2535, July.
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    Cited by:

    1. Amaya, Ivan & Cruz, Jorge & Correa, Rodrigo, 2015. "Harmony Search algorithm: a variant with Self-regulated Fretwidth," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 1127-1152.
    2. Hu, Gang & Du, Bo & Li, Huinan & Wang, Xupeng, 2022. "Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 428-467.
    3. Po-Chou Shih & Chui-Yu Chiu & Chi-Hsun Chou, 2019. "Using Dynamic Adjusting NGHS-ANN for Predicting the Recidivism Rate of Commuted Prisoners," Mathematics, MDPI, vol. 7(12), pages 1-25, December.
    4. Khoroshiltseva, Marina & Slanzi, Debora & Poli, Irene, 2016. "A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices," Applied Energy, Elsevier, vol. 184(C), pages 1400-1410.
    5. Bereg, Sergey & Díaz-Báñez, José-Miguel & Kroher, Nadine & Ventura, Inmaculada, 2019. "Computing melodic templates in oral music traditions," Applied Mathematics and Computation, Elsevier, vol. 344, pages 219-229.

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