Bentley Communities
Bentley Communities
  • Site
  • User
  • Site
  • Search
  • User
  • Welcome
  • Products
  • Support
  • About
  • More
  • Cancel
GenerativeComponents
  • Product Communities
  • More
GenerativeComponents
GenerativeComponents Community Wiki Optimization with the Optimizer node type
    • Sign in
    • +An Overview of GenerativeComponents
    • +Addin Content
    • +Bentley BIM Modeler Accreditation – Program Overview
    • +C# Sample Solution and other Add-ins
    • +GenerativeComponents Solutions
    • +Learn GenerativeComponents
    • +Reference Material
    • Support for GenerativeComponents
    • -Tutorials
      • 3D array copy surface
      • Add RFA data as BuildingContent to ABD with GC Extension
      • Cell Feature
      • Create a Set of Random Points
      • Creation of Global function from Custom function
      • Creation of Parabolic curve
      • Free Form Roof Example
      • GC Excel Feature
      • GenerativeComponents Essentials Course
      • How To Create Surface From Lines & Curves
      • How to Export GC elements
      • How to get concrete sections in the cross-section dialog in Generative Components
      • Landscape Bridge Example
      • +LawCurve
      • List Of Points With A Loop Example
      • Mesh feature 3d print
      • Modular Multiplication On Circle
      • +Operation Node
      • -Optimization with the Optimizer node type
        • +A - Optimization work flow with Optimizer node type
        • B - Non-dominated solutions and the Pareto frontier
        • +C - Optimization samples
      • Palm Tree Modeler
      • Point By Function Example
      • Points On Curve
      • Prime Number Pattern
      • Selection of Points
      • Selection of points based on Query Expressions
      • Selection of points based on range of indices
      • Set a New Property Value to a Set of Selected Nodes
      • Simple Bridge Example
      • Simple Equations To Describe Form Example
      • Simple Free Form Roof Example
      • Sin Tower
      • Skeleton
      • Sunflower Seed Pattern Modelling
      • Surface Division Basic Steps
      • Surface from Rails and Swept Sections
      • The use of Packager in Generative Components
      • +Tools and Techniques
      • Ulam Spiral From A Rectangular Spiral
      • Video Tutorials
      • Video Tutorials - Short Techniques
    • +User Projects
    • +Visualized Parametric Experimentations
    • +zed_Older Content

     
     Questions about this article, topic, or product? Click here. 

    Optimization with the Optimizer node type

    Optimization in GenerativeComponents CONNECT Edition (GC CE) is accessible through the Optimizer node type. Starting with CONNECT Edition Update 1 the Optimizer node type is part of the default installation of GC CE. GC CE is available as companion feature when installing AECOsim Building Designer CONNECT Edition.

    Optimization is a randomized, yet directed, search through a parametric design space. Parametric design space is the N-dimensional space spanned by the N parameters that drive a parametric model. Depending on the resolution of the parameters the total number of potential solutions contained in the design space can be virtually infinite. Given the limitations of computing, even double numbers have finite resolution; therefore, technically, computer-based design spaces are always finite, although for practical purposes they can be considered virtually infinite unless parameter ranges and resolutions are significantly constrained.

    In order to direct the Optimizer’s search, users need to specify objectives, which are simple numeric values, either of type integer or of type double. Users identify to the Optimizer those parameters that they want the Optimizer to manipulate in order to generate design variants based on which new sets of objective values will be computed. The Optimizer evaluates these sets of objective values in order to determine new sets of parameters. Starting with CONNECT Edition Update 2, the Optimizer accepts two sets of objectives, MinimizationObjectives and MaximizationObjectives. Before Update 2, the Optimizer accepted only one set of Objectives all of which were minimized.

    The Optimizer's goal is to minimize and maximize all MinimizationObjectives and MaximizationObjectives, respectively. While this looks like a circular dependency in violation of the GC principle of a directed acyclic graph (DAG), the DAG is actually disrupted in the Optimizer. The Optimizer starts change propagation by updating the set of parameters that were identified to it; changes propagate through the model and update the objective values; those are received by the Optimizer, which does not update anything (of course, once all objectives have been received, the Optimizer will kick off another round of optimization); thus the cycle is disrupted and the principle of the DAG preserved (see figure 1).

    Figure 1 also shows that not all parameters that control the model need to be passed to the Optimizer. The Optimizer will only manipulate those parameters that were identified to the Optimizer node. Similarly, not all objective values that are computed need to be used to direct the search of the Optimizer. This allows debugging of dependencies and then exploration of specific aspects of the design to increase the understanding about the parametric model, influences of the parameters, and the behavior of the design in general.

    The Optimizer uses one specific genetic algorithm (GA). The GA in the Optimizer is an expansion of an algorithm used in Bentley’s engineering tools since about 2005. Originally a single-objective algorithm it has been expanded to a truly multi-objective algorithm.

    • Genetic Algorithm
    • Optimization
    • GenerativeComponents
    • Share
    • History
    • More
    • Cancel
    • Mueller Created by Mueller
    • When: Thu, Jan 4 2018 10:51 AM
    • Revisions: 1
    • Comments: 0
    Recommended
    Related
    Communities
    • Home
    • Getting Started
    • Community Central
    • Products
    • Support
    • Secure File Upload
    • Feedback
    Support and Services
    • Home
    • Product Support
    • Downloads
    • Subscription Services Portal
    Training and Learning
    • Home
    • About Bentley Institute
    • My Learning History
    • Reference Books
    Social Media
    •    LinkedIn
    •    Facebook
    •    Twitter
    •    YouTube
    •    RSS Feed
    •    Email

    © 2021 Bentley Systems, Incorporated  |  Contact Us  |  Privacy |  Terms of Use  |  Cookies