Identifying the Pareto Front from a set of points in a multi-objective space is the most important and also the most time-consuming task in multi-objective optimization. Usually, this is done through so called nondominated sorting. In this package, two efficient algorithms are provided to find the Pareto Front from a large set of multi-objective points.
The basic algorithm is implemented as an mex function. The algorithm considers the logical relationship between dominated and nondominated points to avoid unnecessary comparisons as much as possible so that the overall operations reduced from n x n x m for an n x m problem to r x n x m, where r is the size of the final Pareto Front.
The second algorithm takes the advantage of vectorization of MATLAB to splits the given objective set into several smaller groups to be examined by the first algorithm. Then, the Pareto Fronts of each group are combined as one set to be
re-checked by the first algorithm again to determine the overall Pareto
Front. Numerical tests show that, the overal computation time can be reduced about half of using the first algorithm alone.