New multi-objective optimization technology is presented which considers Pareto frontier as ...

New multi-objective optimization technology is presented which considers Pareto frontier as

a search space for finding Pareto optimal solutions that meet the user’s preferences.

Typically, 80-90% of points evaluated by new optimization algorithms are Pareto optimal,

and the majority of them are located in the user’s area of interest on the Pareto frontier. In

contrast, conventional optimization techniques search for Pareto optimal solutions in the

entire domain, which increases computational effort by orders of magnitude. New

optimization technology is represented by two new algorithms: Multi-Gradient Pathfinder

(MGP), and Hybrid Multi-Gradient Pathfinder (HMGP) (patent pending). MGP is a pure

gradient-based algorithm; it starts from a Pareto-optimal point, and steps along the Pareto

surface in the direction that allows improving a subset of objective functions with higher

priority. HMGP is a hybrid of a gradient-based technique and genetic algorithms (GA); it

works similarly to MGP, but in addition, searches for dominating Pareto frontiers. HMGP is

designed to find the global Pareto frontier and the best Pareto optimal points on this frontier

with respect to preferable objectives. Both algorithms are designed for optimizing very

expensive models, and are able to optimize models ranging from a few to thousands of design

variables.

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