Noesis Solutions, the partner simulation innovation to manufacturers in automotive, aerospace, and other engineering-intense industries, announced that it has released Optimus 10.14. While previous product releases introduced a large number of new CAD & CAE interfaces confirming Optimus as the most open full-process PIDO platform, the latest Optimus 10.14 release focuses on user productivity and optimization excellence. Particle swarm optimization inspired by nature Optimus 10.14 introduces a new Particle Swarm Optimization (PSO) algorithm that can be used for both single-objective and multi-objective optimization. The new PSO algorithm is inspired by swarm intelligence typically found in animal flocks, fish schools and ant colonies. All swarm individuals continuously adjust their positions by making compromises between the ‘local best positions’ pursued by the individuals and the ‘global best positions’ indicated by the entire flock. The new PSO algorithm efficiently handles high-dimensional optimization challenges, supports parallel execution of experiments, and delivers a highly accurate optimal design point or Pareto front. When processing time is most critical, the PSO algorithm is able to match the results of existing genetic and evolutionary algorithms using far less experiments. Additionally, this new Optimus algorithm is extremely easy and robust to use, both for single-objective and multi-objective optimization applications. Clustering for deeper insight or data reduction Cluster Analysis featured in Optimus 10.14 takes post-processing analysis to a higher level. By grouping design points with similar characteristics in separate clusters, Optimus is able to identify valuable correlations between and within clusters. As the new Optimus clustering capability is fully automated, it combines unmatched ease of use and high consistency in delivering accurate results. In addition, Optimus supports visualization tools for easy graphic cluster evaluation including cluster scatter and parallel coordinates charts. Engineers often use cluster analysis to identify interesting subregions in the design space to be explored in greater detail. Clustering may also trace correlations between designs within the same cluster, which may not be visible from an analysis covering all designs. Besides gaining deeper insight through valuable data correlations, Cluster Analysis can save tremendous simulation time. In specific situations, the dataset can be reduced reliably with individual cluster-representative data points instead of using all cluster points. Next to K-means and Hierarchical clustering methods, Optimus incorporates Gaussian Mixture Models (GMM) clustering. This extremely powerful clustering method is ideal for large datasets with varying cluster sizes. Optimus sets itself apart by automatically calculating the appropriate number of clusters for the specific application under investigation. Identifying the most suitable cluster count is critical in obtaining an automated cluster analysis process that consistently delivers accurate clustering results with minimum user interaction.