A hypervolume based approach for minimal visual coverage shortest path

In this paper, the minimal visual coverage shortest path in raster terrain is studied with the proposal of a hypervolume contribution based multiobjective evolutionary approach. The main feature of the presented method is that all individuals in the population are periodically replaced by the selected non-dominated candidates in the archive based on hypervolume contribution, besides the well designed evolutionary operators and some popular techniques such as dominated relation and archive. Our algorithm may obtain well distributed Pareto set approximation efficiently, which is superior to the implementations based on the framework of NSGA-II and SMS EMOA with respect to the hypervolume.