{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T06:32:13Z","timestamp":1768977133124,"version":"3.49.0"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>In the literature, several exploration algorithms have been proposed so far. Among these, L\u00e9vy walk is commonly used since it is proved to be more efficient than the simple random-walk exploration. It is beneficial when targets are sparsely distributed in the search space. However, due to its super-diffusive behavior, some tuning is needed to improve its performance, specifically when targets are clustered. Firefly algorithm is a swarm intelligence-based algorithm useful for intensive search, but its exploration rate is very limited. An efficient and reliable search could be attained by combining the two algorithms since the first one allows exploration space, and the second one encourages its exploitation. In this paper, we propose a swarm intelligence-based search algorithm called L\u00e9vy walk and Firefly-based Algorithm (LFA), which is a hybridization of the two aforementioned algorithms. The algorithm is applied to Multi-Target Search and Multi-Robot Foraging. Numerical experiments to test the performances are conducted on the robotic simulator ARGoS. A comparison with the original firefly algorithm proves the goodness of our contribution.<\/jats:p>","DOI":"10.3390\/bdcc6010022","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T20:24:21Z","timestamp":1645475061000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["LFA: A L\u00e9vy Walk and Firefly-Based Search Algorithm: Application to Multi-Target Search and Multi-Robot Foraging"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7991-0677","authenticated-orcid":false,"given":"Ouarda","family":"Zedadra","sequence":"first","affiliation":[{"name":"LabSTIC, Department of Computer Science, 8 Mai 1945 University, P.O.Box 401, Guelma 24000, Algeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1469-9484","authenticated-orcid":false,"given":"Antonio","family":"Guerrieri","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking (ICAR), CNR\u2014National Research Council of Italy, Via P. Bucci 8\/9C, 87036 Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0236-8541","authenticated-orcid":false,"given":"Hamid","family":"Seridi","sequence":"additional","affiliation":[{"name":"LabSTIC, Department of Computer Science, 8 Mai 1945 University, P.O.Box 401, Guelma 24000, Algeria"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sahin, E. (2005). Swarm Robotics, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/b105069"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"\u015eahin, E., Girgin, S., Bayindir, L., and Turgut, A.E. (2008). Swarm Robotics. Swarm Intelligence, Springer.","DOI":"10.1007\/978-3-540-74089-6_3"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.ins.2016.12.024","article-title":"Firefly algorithm with neighborhood attraction","volume":"382\u2013383","author":"Wang","year":"2017","journal-title":"Inf. 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