Abstract
Harmony search algorithm is a meta-heuristic, nature-inspired optimization algorithm that tries to mimic real-life improvisations that musicians use to generate a harmony that is more pleasing to hear. This paper presents and compares three different types of harmony search algorithms. We start with the implementation of the original HSA. Further, two different modifications were made to the original HSA, the first one uses fitness proportionate selection of harmonies from the HM and is known as biased Roulette harmony search algorithm (BRHSA) and the second one builds on BRHSA by adding simple mathematics to further guide the algorithm toward the desired solution and is called guided biased Roulette harmony search algorithm (GBRHSA). These three variants of HSA were applied on four benchmark test functions on the same machine, and the results obtained after 30 runs were noted down for comparison. It was observed that the results given by the three variants had no specific trend in terms of best result or computational time and the performance of a particular variant was subject to parameters like the kind of function and its search domain. The results presented in this paper can be used as a foundation for the future works that will be done on this algorithm and can help derive an apt variant of HSA for solving a particular problem.
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Yadav, R., Vullamparthi, S., Tapadia, A., Kulkarni, A.J., Kale, P. (2023). Probabilistic Harmony Search Algorithm: Fitness Proportionate Selection Variants. In: Kulkarni, A.J., Mirjalili, S., Udgata, S.K. (eds) Intelligent Systems and Applications. Lecture Notes in Electrical Engineering, vol 959. Springer, Singapore. https://doi.org/10.1007/978-981-19-6581-4_33
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DOI: https://doi.org/10.1007/978-981-19-6581-4_33
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