{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:17:44Z","timestamp":1781108264721,"version":"3.54.1"},"reference-count":0,"publisher":"IGI Global Scientific Publishing","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,10,1]]},"abstract":"<p>This paper proposes a new population-based simplex method for continuous function optimization. The proposed method, called Adaptive Population-based Simplex (APS), is inspired by the Low-Dimensional Simplex Evolution (LDSE) method. LDSE is a recent optimization method, which uses the reflection and contraction steps of the Nelder-Mead Simplex method. Like LDSE, APS uses a population from which different simplexes are selected. In addition, a local search is performed using a hyper-sphere generated around the best individual in a simplex. APS is a tuning-free approach, it is easy to code and easy to understand. APS is compared with five state-of-the-art approaches on 23 functions where five of them are quasi-real-world problems. The experimental results show that APS generally performs better than the other methods on the test functions. In addition, a scalability study has been conducted and the results show that APS can work well with relatively high-dimensional problems.<\/p>","DOI":"10.4018\/ijsir.2016100102","type":"journal-article","created":{"date-parts":[[2016,8,15]],"date-time":"2016-08-15T10:37:52Z","timestamp":1471257472000},"page":"23-51","source":"Crossref","is-referenced-by-count":2,"title":["An Adaptive Population-based Simplex Method for Continuous Optimization"],"prefix":"10.4018","volume":"7","author":[{"given":"Mahamed G.H.","family":"Omran","sequence":"first","affiliation":[{"name":"Department of Computer Science, Gulf University for Science and Technology, Hawally, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maurice","family":"Clerc","sequence":"additional","affiliation":[{"name":"Independent Consultant, Groisy, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","container-title":["International Journal of Swarm Intelligence Research"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=163061","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T15:57:07Z","timestamp":1654099027000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJSIR.2016100102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2016,10,1]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2016,10]]}},"URL":"https:\/\/doi.org\/10.4018\/ijsir.2016100102","relation":{},"ISSN":["1947-9263","1947-9271"],"issn-type":[{"value":"1947-9263","type":"print"},{"value":"1947-9271","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,10,1]]}}}