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Open Access

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Jinglin Wang1, Haibin Ouyang1( ), Zhiyu Zhou1, Steven Li2
School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, China
Graduate School of Business and Law, RMIT University, Melbourne 3000, Australia
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Abstract

Harmony Search (HS) algorithm is highly effective in solving a wide range of real-world engineering optimization problems. However, it still has the problems such as being prone to local optima, low optimization accuracy, and low search efficiency. To address the limitations of the HS algorithm, a novel approach called the Dual-Memory Dynamic Search Harmony Search (DMDS-HS) algorithm is introduced. The main innovations of this algorithm are as follows: Firstly, a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory, creating an effective and selectable trust region to reduce approach blind searching. Furthermore, the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability. Secondly, to boost the algorithm’s convergence speed, a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy. Lastly, the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm’s search strategies, which aim to rationalize the abilities of exploration and exploitation of the algorithm. The results tested on the Computational Experiment Competition on 2017 (CEC2017) test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity, freedom from local optima, and solution accuracy. In addition, applying DMDS-HS to data clustering problems, the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms, which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.

Keywords

optimizationharmony searchdual-memorydynamic searchdata clustering

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Complex System Modeling and Simulation
Volume 3 Issue 4,
December 2023
Pages 261-281
DOI: 10.23919/CSMS.2023.0019
Cite this article:
Wang J, Ouyang H, Zhou Z, et al. Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering. Complex System Modeling and Simulation, 2023, 3(4): 261-281. https://doi.org/10.23919/CSMS.2023.0019

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Received: 30 July 2023
Revised: 06 October 2023
Accepted: 16 October 2023
Published: 07 December 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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