During the last three decades, meta-heuristics, especially evolutionary algorithms and swarm intelligence algorithms are preferred to be employed for solving more and more complex real-world optimization problems. Among them, variants of three classical algorithms, genetic algorithm (GA), particle swarm optimization (PSO), and diffenrential evolution (DE), consists of most of the state-of-the-art algorithms currently. However, one human music composition inspired harmony search (HS) proposed shortly after them may be undervalued to be further and adequately developed. Due to its different search paradigm and efficient performance, its future advanced variants may have potential consists the state-of-the-art meta-heuristics. In this paper, we propose a differential search integrated harmony search algorithm (DHS) and compare it with original HS, three classical EAs, and one newly proposed artificial hummingbird algorithm, on the CEC2024 competition benchmark suite. The experimental statistical results and convergence analysis show that the DHS outperforms all these 5 algorithms and thus shows a well potential to be advanced in the future to reach and even surpass the current GA, PSO, and DE state-of-the-art variants.
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