Changes between Version 5 and Version 6 of Documentation/Reference/Particle Swarm Optimization


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Timestamp:
03/31/11 14:50:13 (11 years ago)
Author:
mkofler
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  • Documentation/Reference/Particle Swarm Optimization

    v5 v6  
    4242=== Parameter Adjustment ===
    4343
    44 Like many other metheuristics, the PSO algorithm frequently faces the problems of being trapped in local optima. Balancing the global exploration and local exploitation abilities of PSO is therefore very important.
     44Like many other metheuristics, the PSO algorithm frequently faces the problems of being trapped in local optima. Balancing the global exploration and local exploitation abilities of PSO is therefore very important. Important PSO parameters that should be tweaked for every problem (or even instance):
     45
     46'''Inertia Weight'''
     47The inertia weight parameter was introduced in 1998 by Shi and Eberhart. The idea was to use a maximum velocity and set the velocity bounds to the dynamic range of the solution space. One can further improve results by adjusting the inertia weight dynamically during the optimization run (via fuzzy optimization, by linear decreasing or increasing or randomizing the parameter).
     48
     49'''Velocity Bounds'''
     50Another important parameter is the minimum/maximum velocity bounds vector. Particle velocities on each dimension are clamped to a certain velocity range by the parameter `VelocityBounds`. Therefore `VelocityBounds` affects the maximum global exploration ability of PSO, since
     51* large velocity --> global exploration and
     52* small velocity --> local exploitation.
    4553
    4654'''Parameter Tuning  '''
    4755
    48 A recent paper by [#References (Pedersen 2010)] provides a most helpful table of PSO parameters that have been tuned for different optimization scenarios. We recommend them as a first starting point when optimizing new problems. Some of the settings (like using a negative inertia weight) may seem quirky, but we also got some very good results with those settings.
    49 
    50 '''Velocity Bounds'''
    51 Another is to adjust the minimum/maximum velocity bounds vector. Particle velocities on each dimension are clamped to a certain velocity range. The parameter `VelocityBounds` controls the maximum global exploration ability of PSO.
    52 * large velocity --> global exploration
    53 * small velocity --> local exploitation
    54 
    55 '''Inertia Weight'''
    56 The inertia weight parameter was introduced in 1998 by Shi and Eberhart. The idea was to use a maximum velocity ad set the velocity bounds to One common strategy is to adjust the inertia weight dynamically during the optimization run (via fuzzy optimization, by linear decreasing or increasing or randomizing the parameter).
     56A recent paper by [#References (Pedersen 2010)] provides a most helpful table of PSO parameters that have been tuned via meta/optimization for different optimization scenarios. We recommend them as a first starting point when optimizing new problems. Some of the settings (like using a negative inertia weight) may seem quirky, but we also got some very good results with those settings.
    5757
    5858It is possible to let the algorithm adjust some parameters dynamically during runtime.