ResearchArticle DingoOptimizer:ANature-InspiredMetaheuristicApproachfor Engineering Problems AmitKumarBairwa , 1 Sandeep Joshi , 1 andDilbagSingh 2 1 Manipal University Jaipur, Rajasthan, Jaipur, India 2 Bennett University, Uttar Pradesh, Noida, India CorrespondenceshouldbeaddressedtoSandeepJoshi;sjoshinew@yahoo.com Received 23 July 2020; Revised 8 October 2020; Accepted 16 December 2020; Published 10 June 2021 AcademicEditor:YannFavennec Copyright©2021AmitKumarBairwaetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Optimizationisabuzzword,wheneverresearchersthinkofengineeringproblems.ispaperpresentsanewmetaheuristicnamed dingooptimizer(DOX)whichismotivatedbythebehaviorofdingo(Canisfamiliarisdingo).eoverallconceptistodevelopthis methodinvolvingthecollaborativeandsocialbehaviorofdingoes.edevelopedalgorithmisbasedonthehuntingbehaviorof dingoesthatincludesexploration,encircling,andexploitation.Alltheabovepreyhuntingstepsaremodeledmathematicallyand areimplementedinthesimulatortotesttheperformanceoftheproposedalgorithm.Comparativeanalysesaredrawnamongthe proposedapproachandgreywolfoptimizer(GWO)andparticleswarmoptimizer(PSO).Someofthewell-knowntestfunctions are used for the comparative study of this work. e results reveal that the dingo optimizer performed significantly better than other nature-inspired algorithms. 1.Introduction echallengesofthemodernworldarecomposedofvarious goalsthatmustbeoptimizedatthesametime.Optimization isaprocessthatseeksoneormoresolutionstotheproblem thatleadstotheextremevaluesofoneormoreobjective[1]. eoptimizationcan,therefore,bedonebasedonsingleor multiple objective functions [2–4]. Keepingthisinthemind,thereisarequirementofnew metaheuristic-basedsolutiontoreducetheburdenofanyof themodeldesigning.eobjectiveofthispaperistodevelop anature-basedalgorithmcalleddingooptimizer,whichcan be abbreviated as DOX. It is based on dingo’s social hier- archy and prey hunting behavior. Metaheuristic algorithms are remarkably common due to its nature of flexibility, simplicity, less mathematical complexity,andavoidanceoflocaloptima.Ifwetalkabout flexibility, then it means we can use such algorithms in a wide variety of engineering problems. Such algorithms provide satisfactory results for many of the complex problems[5].Itissimplebecauseitisinspiredbynaturelike animal behavior to accomplish a particular task, physical phenomena, and other evolutionary behavior. One of the main reasons to use the metaheuristics in real-life problems is that almost all the optimization solu- tions start with the random processes, and for such solu- tions, there is no need to find out the optimum. Metaheuristic algorithms are very powerful in terms of finding local optima compared with the traditional opti- mizationalgorithms.Findingtherealsearchspaceinthereal worldproblemisverymuchcomplicatedbecauseoffinding with lots of local optima in the search. at is the reason metaheuristic algorithms are most suitable to find out such challenging issues. ere are so many metaheuristic algorithms proposed everyyear,andtheyshowthepromisingresultwithrespect totheengineeringproblem.However,daybydaythenature and complexity of new applications are introducing with new challenges. And, it might not be possible to solve the particularproblemwiththeguarantee.ismotivatesusto develop a new metaheuristics algorithm as dingo optimizer (DOX).Also,themethodwhichismathematicallymodeled Hindawi Mathematical Problems in Engineering Volume 2021, Article ID 2571863, 12 pages https://doi.org/10.1155/2021/2571863