Abstract
This paper introduces a novel murmuration-flight-based dispersive optimization algorithm (MDO) inspired by the natural phenomena of starlings’ murmuration, flight patterns of migrating birds, and dispersive migration. To the best of our knowledge, the proposed algorithm is the first of its kind to utilize Lévy flights to initialize the first population of solutions, thereby ensuring better exploration of the search space from the starting point of the optimization process. Additionally, the starling murmuration leads to better local and global search ability. Captain selection and dispersive migration give the proposed algorithm greater exploitation power. It has few tunable parameters and can be easily applied to various problem domains. Extensive tests and experiments show that the MDO delivers promising and competitive results over other algorithms, and its applicability is also checked statistically by performing a significance test. One of the most complex problems in health IoTs is how to preserve sensitive and personal patient data while addressing the main concerns of data integrity and security in modern health information and telemedicine systems. Hence, the MDO is applied to solve the optimum key-based image encryption problem to showcase its usefulness in real-world applications. Simplicity, efficiency, and better adaptability make the proposed method a strong contender for solving complex optimization problems.
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Bharti, V., Biswas, B. & Shukla, K.K. MDO: a novel murmuration-flight based dispersive optimization algorithm and its application to image security. J Ambient Intell Human Comput 14, 4809–4826 (2023). https://doi.org/10.1007/s12652-023-04537-5
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DOI: https://doi.org/10.1007/s12652-023-04537-5