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However, for Dynamic Multi-Objective Optimisation (DMOO), no standard benchmark functions are used. A number of DMOOPs have been proposed in recent years. However, no comprehensive overview of DMOOPs exist in the literature. Therefore, choosing which benchmark functions to use is not a trivial task. This article seeks to address this gap in the DMOO literature by providing a comprehensive overview of proposed DMOOPs, and proposing characteristics that an ideal DMOO benchmark function suite should exhibit. In addition, DMOOPs are proposed for each characteristic. Shortcomings of current DMOOPs that do not address certain characteristics of an ideal benchmark suite are highlighted. These identified shortcomings are addressed by proposing new DMOO benchmark functions with complicated Pareto-Optimal Sets (POSs), and approaches to develop DMOOPs with either an isolated or deceptive Pareto-Optimal Front (POF). In addition, DMOO application areas and real-world DMOOPs are discussed.<\/jats:p>","DOI":"10.1145\/2517649","type":"journal-article","created":{"date-parts":[[2014,2,7]],"date-time":"2014-02-07T14:22:52Z","timestamp":1391782972000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":65,"title":["Benchmarks for dynamic multi-objective optimisation algorithms"],"prefix":"10.1145","volume":"46","author":[{"given":"Mard\u00e9","family":"Helbig","sequence":"first","affiliation":[{"name":"CSIR, Meraka Institute and University of Pretoria, Department of Computer Science, Pretoria, South Africa"}]},{"given":"Andries P.","family":"Engelbrecht","sequence":"additional","affiliation":[{"name":"University of Pretoria, Department of Computer Science, Pretoria, South Africa"}]}],"member":"320","published-online":{"date-parts":[[2014,1]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"P. 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