Advanced Optimization

LMS Virtual.Lab Advanced Optimization

Advanced Optimization incorporates additional global and discrete optimization methods as well as multi-objective optimization and robust design techniques.

Global Optimization

Global Optimization with three different state-of-the-art algorithms [Differential Evolution (DE), Self-adaptive Evolution (SE) and Simulated Annealing (SA)] solves general constrained optimization problems. These algorithms have a high probability of finding a global optimum. Discrete Optimization solves general constrained optimization problems including a mixture of continuous and discrete variables. A choice of discrete variables is available: either integer values only, or a catalog of real values, or a list of strings. Special optimization search routines effectively take into account the discrete character of the input variables.

Multi-Objectives Optimization

The Multi-Objective Optimization (MOO) module contains 9 MOO methods and allows users to efficiently optimize designs with two or more often competing objectives. MOO methods consist of local optimization methods, including the normal-boundary intersection method, the weighted objective method, the weighted Tchebycheff method and the min-max optimum method and the multi-objective optimization methods: non-dominated sorting evolutionary (NSEA and NSEA+) algorithms which calculate Pareto fronts. In addition, the trade-off method, the
hierarchical method, the distance function method (Euclidian norm), the distance function method (goal programming) and the global criterion method compute individual Pareto points. New powerful post-processing functionalities provide more insight capabilities to explore the engineering design, including Pareto plots.

Robust Design

Taking into account the influence of such parameter distributions, users can study the result variation around the optimal value and build higher-quality products by making the design more robust (less sensitive to parameter variability) and more reliable (lower probability of exceeding the design constraints).

Covering a range of industries, LMS application cases let you discover how LMS solutions help our customers solve their real-life engineering challenges.