The objective of this paper is to describe an opposite gradient initialization concept with mean-variance mapping optimization (OGI-MVMO). OGI-MVMO is an optimization based on the actual manifold of objective function whereas original MVMO based stochastic optimization. Generating the new candidate solution to speed up the solution finding and accuracy of solution are important purposes. The OGI-MVMO algorithm consist of 2 steps: the primary step is generating new solution by OGI and also the second step is mutation between every of selected candidate solution supported the mean and variance of the population. The results showed that OGI-MVMO algorithm has better performance than other algorithm include the original MVMO for 15 real-parameter single objective functions.


Continuous function Mean-variance mapping optimization Opposite gradient initialization search Optimization 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Chulalongkorn UniversityBangkokThailand

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