Opposition-based learning multi-verse optimizer with disruption operator for optimization problems

Multi-verse optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack diversity which may trapping local minima, and premature convergence. This paper introduces two steps improving basic algorithm. The first step using opposition-based learning (OBL) MVO, called OMVO. OBL aids speed up searching technique for selecting better generation candidate solutions MVO. second stage, OMVOD, combines disturbance operator (DO) OMVO improve consistency chosen solution by providing chance given problem with high fitness value increase diversity. To test performance proposed models, fifteen CEC 2015 benchmark functions problems, thirty 2017 seven 2011 real-world were both phases enhancement. step, known as incorporates disruption accuracy giving while also increasing variety. Fifteen upgrade assess models.