Configuring the Multi-Objective Particle Swarm Technique

Particle swarm optimization mimics the social behavior of animal groups, such as flocks of birds or fish shoals. The process of finding an optimal design point is similar to the food-foraging activity of animals.

See Also
Multi-Objective Particle Swarm Technique
Configuring the Technique and the Execution Options
  1. From the Flow section of the action bar, click Optimization and drop it on the process diagram.
  2. Double-click Optimization .
    The Optimization Editor appears
  3. From the General tab's Optimization Technique list, select Multi-Object Particle Swarm.
  4. In the Optimization Technique Options area, enter or select the following:
    OptionDescription
    Maximum Iterations The maximum number of iterations during optimization. The default value is 50. Other possible values are ≥   1 .
    Number of Particles The size of the population. The number of generated designs during optimization is (number of particles) × (maximum iterations). The default value is 10. Other possible values are ≥   1 .
    Inertia The inertia of each particle in the swarm. The default value is 0.9. It is recommended that you choose a value less than 2 for a good compromise between exploration and convergence. Other possible values are >   0 .
    Global Increment The maximum increment to the particle position based on the global best position. The default value is 0.9. Other possible values are >   0 .
    Particle Increment The maximum increment to the particle position based on the particle best position. The default value is 0.9. Other possible values are >   0 .
    Maximum Velocity The maximum value of the particle velocity. The default value is 0.1. Using a higher limit enables exploration of larger regions of the design space at the cost of convergence. Other possible values are >   0 .
    Maximum Failed Runs The maximum number of failed subflow evaluations that can be tolerated by the optimization technique. If the number of failed runs exceeds this value, the optimization adapter will terminate execution. To disable this feature, set this option to any negative value (for example, –1). When this option is set to a negative value, the optimization will continue execution despite any number of failed subflow runs.
    Failed Run Penalty Value The Penalty parameter that is used for all failed subflow runs. The default value is 1 ⁢   ×   ⁢ 10 30 .
    Failed Run Objective Value The value of the Objective parameter that is used for all failed subflow runs. The default value is 1 ⁢   ×   ⁢ 10 30 .
    Use fixed random seed If this option is selected, the random number generator used by the optimization algorithm is seeded using the value specified in the corresponding text box.

    If this option is not selected, the random number generator is seeded by using the clock time at the moment of execution.
    Random seed value All executions of the Optimization adapter will use the same sequence of random numbers and, therefore, will produce the same design points. This arrangement is useful for debugging the optimization process when it is necessary to reproduce the same sequence of design points.
  5. Click Ok to save your changes and to close the Optimization Editor.