{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T20:08:54Z","timestamp":1767211734177,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Quantum computing has opened up various opportunities for the enhancement of computational power in the coming decades. We can design algorithms inspired by the principles of quantum computing, without implementing in quantum computing infrastructure. In this paper, we present the quantum predator\u2013prey algorithm (QPPA), which fuses the fundamentals of quantum computing and swarm optimization based on a predator\u2013prey algorithm. Our results demonstrate the efficacy of QPPA in solving complex real-parameter optimization problems with better accuracy when compared to related algorithms in the literature. QPPA achieves highly rapid convergence for relatively low- and high-dimensional optimization problems and outperforms selected traditional and advanced algorithms. This motivates the application of QPPA to real-world application problems.<\/jats:p>","DOI":"10.3390\/a17010033","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T09:24:11Z","timestamp":1705051451000},"page":"33","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Quantum-Inspired Predator\u2013Prey Algorithm for Real-Parameter Optimization"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9435-5328","authenticated-orcid":false,"given":"Azal Ahmad","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Chemistry, Indian Institute of Technology Guwahati, Guwahati 781001, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2683-1871","authenticated-orcid":false,"given":"Salman","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati 781001, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6353-1464","authenticated-orcid":false,"given":"Rohitash","family":"Chandra","sequence":"additional","affiliation":[{"name":"Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, University of New South Wales, Sydney 2033, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1007\/s10462-017-9605-z","article-title":"Metaheuristic research: A comprehensive survey","volume":"52","author":"Hussain","year":"2019","journal-title":"Artif. 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