{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T20:20:43Z","timestamp":1777494043773,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,10,15]],"date-time":"2019-10-15T00:00:00Z","timestamp":1571097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61673354"],"award-info":[{"award-number":["No. 61673354"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["CUGGC03"],"award-info":[{"award-number":["CUGGC03"]}]},{"name":"the State Key Lab of Digital Manufacturing Equipment &amp; Technology, Huazhong University of Science &amp; Technology","award":["DMETKF2018020"],"award-info":[{"award-number":["DMETKF2018020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Pre-stack amplitude variation with offset (AVO) elastic parameter inversion is a nonlinear, multi-solution optimisation problem. The techniques that combine intelligent optimisation algorithms and AVO inversion provide an effective identification method for oil and gas exploration. However, these techniques also have shortcomings in solving nonlinear geophysical inversion problems. The evolutionary optimisation algorithms have recognised disadvantages, such as the tendency of convergence to a local optimum resulting in poor local optimisation performance when dealing with multimodal search problems, decreasing diversity and leading to the prematurity of the population as the number of evolutionary iterations increases. The pre-stack AVO elastic parameter inversion is nonlinear with slow convergence, while the pigeon-inspired optimisation (PIO) algorithm has the advantage of fast convergence and better optimisation characteristics. In this study, based on the characteristics of the pre-stack AVO elastic parameter inversion problem, an improved PIO algorithm (IPIO) is proposed by introducing the particle swarm optimisation (PSO) algorithm, an inverse factor, and a Gaussian factor into the PIO algorithm. The experimental comparisons indicate that the proposed IPIO algorithm can achieve better inversion results.<\/jats:p>","DOI":"10.3390\/sym11101291","type":"journal-article","created":{"date-parts":[[2019,10,16]],"date-time":"2019-10-16T03:32:54Z","timestamp":1571196774000},"page":"1291","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Improved Pigeon-Inspired Optimisation Algorithm and Its Application in Parameter Inversion"],"prefix":"10.3390","volume":"11","author":[{"given":"Hanmin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan Institute of Ship Building Technology, Wuhan 430050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuesong","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratary of Intelligent Geo-Information Processing, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghua","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,15]]},"reference":[{"key":"ref_1","unstructured":"Kennedy, J. 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