{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T02:08:01Z","timestamp":1740103681432,"version":"3.37.3"},"reference-count":49,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,4,6]],"date-time":"2020-04-06T00:00:00Z","timestamp":1586131200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1904123","162300410177","19A520026"],"award-info":[{"award-number":["U1904123","162300410177","19A520026"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["U1904123","162300410177","19A520026"],"award-info":[{"award-number":["U1904123","162300410177","19A520026"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research Program of Application Foundation and Advanced Technology of Henan Province","award":["U1904123","162300410177","19A520026"],"award-info":[{"award-number":["U1904123","162300410177","19A520026"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2020,4,6]]},"abstract":"<jats:p>Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO\u2019s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats\u2019 updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats\u2019 updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).<\/jats:p>","DOI":"10.1155\/2020\/7824785","type":"journal-article","created":{"date-parts":[[2020,4,6]],"date-time":"2020-04-06T23:31:23Z","timestamp":1586215883000},"page":"1-19","source":"Crossref","is-referenced-by-count":3,"title":["Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP"],"prefix":"10.1155","volume":"2020","author":[{"given":"Xinming","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan 453007, China"}]},{"given":"Doudou","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan 453007, 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