regrouping particle swarm optimization: a new global optimization algorithm with improved...
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Regrouping Particle Swarm Optimization: A New Global Optimization Algorithm with
Improved Performance Consistency Across
BenchmarksGeorge I. Evers
Advisor: Dr. Mounir Ben GhaliaElectrical Engineering Department
The University of Texas – Pan American
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OutlineI. From Physics to PSOII. Visual Illustration of Stagnation
& the Regrouping MethodIII. RegPSO FormulationIV. Graph of Solution QualityV. Statistical Comparison with
Basic PSOVI. SummaryVII. Future Work
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How PSO Derives from Standard Physics
Equations
I. From Physics to PSO
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From Physics to PSO
Displacement Formula of Physics:
20 0
1
2x x v t at
assuming constant acceleration over the time period
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From Physics to PSO
Iterative Version:
Using 1 time unit between iterations:• t = (k + 1) – k = 1 iteration per update• t2 = 1 iteration2 per update• For practical purposes, t drops out of the equation.
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1( 1) ( ) ( ) ( )
2x k x k v k a k
From Physics to PSO
Subscript “i” Used for Particle Index:
(All particles follow the same rule.)
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1( 1) ( ) ( ) ( ).
2i i i ix k x k v k a k
From Physics to PSO
Particles are physical conceptualizations accelerating according to social andcognitive influences.
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From Physics to PSO
Cognitive AccelerationThe cognitive acceleration is proportional to(i) the distance, , of a particle from its personal best, and (ii) the cognitive acceleration coefficient, .
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( ) ( )i ip k x k
1c
From Physics to PSO
Social AccelerationThe social acceleration is proportional to(i) the distance, , of a particle from its global best, and (ii) the social acceleration coefficient, .
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( ) ( )ig k x k
2c
From Physics to PSO
Total AccelerationThe overall acceleration can therefore be
written as
Substitution then leads from
to
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1 2( ) c ( ) ( ) c ( ) ( ) .i i i ia k p k x k g k x k
1 2
1 1( 1) ( ) ( ) c ( ) ( ) c ( ) ( ) .
2 2i i i i i ix k x k v k p k x k g k x k
1( 1) ( ) ( ) ( )
2i i i ix k x k v k a k
From Physics to PSO
Total AccelerationIn place of constant , a pseudo-random
number with an expected value of is generated per dimension to add anelement of stochasm to the algorithm.
In this manner
becomes
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1 1 2 2( 1) ( ) ( ) c ( ) ( ) c ( ) ( ) .i i i i i i i ix k x k v k r p k x k r g k x k
1 2
1 1( 1) ( ) ( ) c ( ) ( ) c ( ) ( )
2 2i i i i i ix k x k v k p k x k g k x k
1
2 1
2
From Physics to PSO
Simulating FrictionTo prevent velocities from growing out of control, only
a fraction of the velocity is carried over to the next iteration. This is accomplished by introducing an inertia weight, , which is set less than 1.
In this manner
becomes
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1 1 2 2( 1) ( ) ( ) c ( ) ( ) c ( ) ( ) .i i i i i i i ix k x k v k r p k x k r g k x k
1 2
1 1( 1) ( ) ( ) c ( ) ( ) c ( ) ( )
2 2i i i i i ix k x k v k p k x k g k x k
From Physics to PSO
Velocity and Position Updates
The previous equation is separated into two more succinct equations, allowing velocities and positions to be recorded and analyzed separately.
1 1 2 2( 1) ( ) c ( ) ( ) ( ) c ( ) ( ) ( )
( 1) ( ) ( 1).
i ii i i i i
i i i
v k v k r k p k x k r k g k x k
x k x k v k
Velocity UpdateEquation
Position UpdateEquation
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The Main Obstacle: Premature Convergence/
Stagnation
II. Visual Example of Stagnation& The Regrouping Method
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Rastrigin BenchmarkUsed to Illustrate Stagnation
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Swarm Initialization
Particles 1 and 3 are selected
to visually illustrate how velocities and positions are
updated.
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Velocities are
randomly initialized
to lie between[-vmax,
vmax] per dimension.
First Velocity Updates
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The velocities of the previous
iteration are reduced by the inertia weight to produce the
inertial components in
red.
Particle 6 found the
best function value, which
it communicates to its friends.
Social acceleration is shown in
blue.
First Position Updates
Particle 1 found a new
personal best, but particle 3
did not.
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Particles moved along the resultant
velocity vectors to their new positions (Page Up,
Page Down to see this).
Second Velocity Updates
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Particle 3 is pulled
cognitively toward its
personal best and socially toward the global best
while experiencing
inertia.Particle 1 is at its personal best, so it
experiences only inertia and social
acceleration.
Second Position Updates
Particle 3 found a new
personal best, while particle
1 did not.
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Particles again moved along
their resultant velocity vectors
to new positions (Page Up, Page Down
to see this).
Swarm Snapshots
Having seen how particles iteratively update their positions, the following slides show the swarm state each 10 iterations to track the progression from initialization to eventual solution.
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Swarm Initialization at Iteration 0
Particles are randomly initialized within the original initialization space.
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Rewinding to monitor collective behavior from the
beginning….
Swarm Collapsing at Iteration 10
Particles are converging to a local minimizer near [2,0] via their attraction to the global best in that vicinity.
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Exploratory Momenta at Iteration 20
Momenta and cognitive accelerations keep particles searching prior to settling down.
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Convergence in Progressat Iteration 30
Personal bests move closer to the global best and momenta wane as no better global best is found. Particles continue converging to the local minimizer near [2,0].
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Momenta Waning at Iteration 40
Momenta continue to wane as particles are repeatedly pulled toward (a) the global best very near [2,0] and (b) their own personal bests in the same vicinity.
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Mostly Converged at Iteration 50
Most particles are improving their approximation of the local minimizer found, while two particles still have some momenta.
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Momenta Waning at Iteration 60
The final two particles are collapsing upon the global best while the remaining particles are refining the solution.
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Momenta Waning at Iteration 70
All particles are in the same general vicinity.
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Cognitive Acceleration at Iteration 80
At least one particle still has some exploratory momentum.
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Premature Convergence Detected
at Iteration 102
All particles have converged to within 0.011% of the diameter of the initialization space. It is important to allow particles to refine each solution before regrouping since they have no prior knowledge of which solution is the global minimizer.
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Options for Dealing with Stagnation
• Terminate the search rather than wasting computations while stagnated.
• Allow the search to continue and hope for solution refinement.
• Restart particles from new positions and look for a better solution.
• Somehow flag solutions already found so that each restart finds new solutions, and continue restarting until no better solutions are found.
• Reinvigorate the swarm with diversity to continue the current search for the global minimizer.
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“Regrouping” Definition
Regroup: “to reorganize (as after a setback)for renewed activity”
– Merriam Webster’s online dictionary
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Regrouping at Iteration 103
Regrouping is more efficient than restarting on the original initialization space.
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Exploration at Iteration 113
“Gbest” PSO continues as usual within the new regrouping space. Particles move toward the global best with new momenta, personal bests, and positions/perspectives. 35
Swarm Migration at Iteration 123
The swarm is migrating toward a better region discovered by an exploring particle near [1,0].
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Differences of Opinion at Iteration 133
Some particles are refining a local minimizer near [1,0] while others continue exploring in the vicinity.
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Solution Comparison at Iteration 143
Cognition pulls some particles back to the local well containing a local minimizer near [1, 0].
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Solution Comparison at Iteration 153
Cognition and momenta keep particles moving as momenta wane.
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Unconvinced of Optimality on Horizontal Dimension
at Iteration 163
There is still some uncertainty on the horizontal dimension.
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New Well Agreed Uponat Iteration 173
All particles agree that the new well is better than the previous.
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Waning Momenta at Iteration 183
Momenta wane.
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Premature Convergence Detected Again at Iteration
219
Regrouping improved the function value from approximately 4 to approximately 1, and premature convergence is detected again.
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Swarm Regrouped Againat Iteration 220
The swarm is regrouped a second time.
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Best Well Foundat Iteration 230
The well containing the global minimizer is discovered.
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Swarm Migrationat Iteration 240
The swarm migrates to the newly found well.
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Convergence at Iteration 250
Particles swarm to the newly found well due to its higher quality minimizer.
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Cognition at Iteration 260
Momenta carry particles beyond the well.
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Convergence at Iteration 270
Solution refinement of the global minimizer is in progress.
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Regrouping PSO (RegPSO) Formulation
III. RegPSO Formula
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Regrouping PSO (RegPSO)Detection of Premature
Convergence
1 2
1, ,
, ,...,
( ) max ( ) ( )
r r r rd
r r
ii s
range range range range
diam range
k x k g k
Range of theSearch Space
Diameter of theSearch Space
Maximum Euclidean Distance from Global Best
Terminate Wh
DDDDDDDDDDDDDD
DDDDDDDDDDDDDD
norm
( )
( )rk
diam
en Maximum Distance from Global Best is Less Than
a User - Specified Percentage of the Diameter of the Search Space
represents
the search space for
regrouping index .
r
r
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Regrouping PSO (RegPSO)Regrouping the Swarm
,1, ,
0
1 2
max
( ) min ( ),
, ,...,
1
j i j ji s
rj j j
r r r rd
i i
x k g k
range range
range range range range
x k g k r ra
Uncertaintyper Dimension
Range of New Search Space
NewSearch Space Centered at Global Best
DDDDDDDDDDDDDD
1 2
1( ) ( )
2
where , ,...,
with each (0,1) randomly selected.i i i
i
r r
i d
j
nge range
r r r r
r U
DDDDDDDDDDDDDDDDDDDDDDDDDDDD
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Regrouping PSO (RegPSO)High-Level Pseudo Code
DoRun Gbest PSO until premature convergence.Regroup the swarm.Re-calculate the velocity clamping value based on
the range of the new initialization space.Re-initialize velocities.Re-initialize personal bests.Remember the global best.
Until Search Termination
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Effectiveness of RegPSO Demonstrated Graphically
IV. Graphical Comparison of Mean Function Values
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Mean Behavior on 30D Rastrigin A swarm size of 20 suffices for RegPSO to approximate the global minimizer of the 30-D Rastrigin and reduce the cost function to approximately true minimum across all 50 trials.
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Effectiveness of RegPSO Demonstrated Statistically
V. Statistical Comparison
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Regrouping PSO (RegPSO)Compared to Gbest, Lbest PSO
RegPSO Compared to Gbest PSO & Lbest PSO of neighborhood size 2 s = 20, c1 = c2 = 1.49618, 50 trial sets, 800,000 function evaluations RegPSO used 4 11.1 10 ; 1.2 ; 100,000 evaluations max per grouping. Benchmark d Gbest PSO
0.5,
0.72984
Gbest PSO 0.15,
0.9 to 0.4
Lbest PSO 0.5,
0.72984
Lbest PSO 0.15,
0.9 to 0.4
RegPSO 0.5,
0.72984
Ackley 30 Mean:
3.6524 1.1191e-014
0.046206 1.0623e-014 5.2345e-007
Griewangk 30 Mean:
0.055008 0.022023 9.1051e-003 0.012538 0.013861
Quadric 30 Mean:
4.1822e-75 2.3189e-014 3.4340e-012 5.9577e-022 3.1351e-010
Quartic with noise
30 Mean:
0.0039438 0.0015241 1.2630e-002 0.0025417 0.00064366
Rastrigin 30 Mean:
71.63686 25.252 52.812 31.2746 2.6824e-011
Rosenbrock 30 Mean:
2.06915 18.859 2.6106 1.0713 0.0039351
Schaffer’s f6 2 Mean:
0.0033034 0 1.2025e-003 0 0
Sphere 30 Mean:
2.4703e-323 1.0834e-094 2.0146e-160 2.1967e-215 9.2696e-015
Weighted Sphere 30 Mean:
1.0869e-321 4.4182e-093 6.5519e-158 1.2102e-225 9.8177e-014
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Summary
By regrouping the swarm within an efficiently sized regrouping space when premature convergence is detected, RegPSO considerably improves performance consistency, as demonstrated with a suite of popular benchmarks.
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Future Work
Theoretical Improvements• Give the algorithm the ability to progress
from regrouping to a solution refinement phase.
Testing• NP hard problems• Applications to real-world problems
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