2009
- A Study on the Convergence of Multiobjective Evolutionary Algorithms
- High computational cost has been a major impediment to the widespread use of evolutionary algorithms in industry. While the clock time for optimization
- Optimal Resource Allocation for Genetic Algorithm Based Multi-Objective Optimization with 1000 Simulations
- This study pertains to practical application of the GA for industrial applications where only a limited number of simulations can be afforded. Specifically, an attempt is made to find the optimal allocation of the total simulation budget (population size and number of generations) for...
- Decision Making in Multi-Objective Optimization for Industrial Applications – Data Mining and Visualization of Pareto Data
- Data mining and visualization techniques for high dimensional data provide helpful information to substantially augment the decision making (alternative design selection) in multi-objective optimization environment
- New Developments in LS-OPT 4.0 - Outlook to V4.1
- Adaptive Simulated Annealing for Global Optimization in LS-OPT
- The efficient search of global optimal solutions is an important contemporary subject. Different optimization methods tackle the search in different ways. The gradient based methods are among the fastest optimization methods but the final optimal solution depends on the starting point.The global search using these methods is carried out by providing many starting points. Other optimization methods...
- Reliability-based Multi-Objective Optimization and Visualization using LS-OPT® Version 4
- This study the multi-objective optimization of a realistic crashworthiness problem with special reference to the incorporation of uncertainty and the visualization of the Pareto Optimal Frontier(POF). LS-OPT® and LS-DYNA® are used for the optimization based on the C2500 truck model developed by NHTSA. The design problems is set up as a Reliability-Based Design Optimization (RBDO) problem which includes specifications...
- Aplication Examples of Optimization and Reliability studies in Automotive Industry.
- The aim this paper is to summarize several optimization an robustness applications, which have been performed over the past years in automotive industry with LS-OPT. The examples include Multi-Objective Optimization (MOO), Multi-Load Case Optimization and Reliability Based Design Optimization (RBDO). In addition, user-friendly visualization of optimization and stochastic results is demonstrated.
