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ADAPTIVE PARAMETER SELECTION IN COMPREHENSIVE LEARNING PARTICLE SWARM OPTIMIZERAuthors: Mohammad Hasanzadeh,Mohammad Reza Meybodi andMohammad Mehdi Ebadzdeh
Soft Computing Laboratory
OUTLINE
▪ Swarm Intelligence (SI)
▪ Particle Swarm Optimization (PSO)
▪ Comprehensive Learning PSO (CLPSO)
▪ Learning Automata (LA)
▪ Main Idea
▪ Results
▪ Conclusion
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SWARM INTELLIGENCE (SI) 3
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PARTICLE SWARM OPTIMIZATION (PSO)• Introduced by Kennedy and Eberhart in 1995• PSO Imitates animals social behavior• Each particle consists of Position and Velocity vectors • Each particle of PSO share a individual information (pbest)• Population of PSO share a social information (gbest)
• Vt+1 = WVt + C1R1(Pi - Xt) + C2R2(Pg - Xt)
• Xt+1 = Xt + Vt+1
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Social Life
Individual Life
VelocityPosition
VISUALIZING PSO
5C2 r
2 (Pi –
Xt )
• Comprehensive Learning PSO introduced by Liang, Qin andSuganthan in 2006
• Incorporate learning from more previous best particles.
• Vt+1 = WVt + C1R1(Pf(i) - Xt)
• Xt+1 = Xt + Vt+1
• fi = [fi(1), fi(2), …, fi(D) ]
COMPREHENSIVE LEARNING PSO (CLPSO)
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Exemplar Function
LEARNING AUTOMATA (LA)• Introduced by Tsetlin in 1960s and Surveyed by Narendra
and Thathachar 1974• Autonomous decision making components• LA consists of the following components:
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Action Set Probability Vector Learning Algorithm
Learning Automata
Random Environment
Action
Reinforcement Signal
• Parameter Selection• Tuning Refreshing Gap Parameter (m)
• Benchmark structure
• Dimensionality
• Population size
• Exploration
• Exploitation
MAIN IDEA
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CLPSO Population Exemplars
New Exemplar
Lear
n F
rom
Ex
em
pla
rs
Generation mod m
▪ Macroscopic behavior of PSO population
▪ Refreshing gap adjustment by LA
▪ Triple action LA
▪ Reinforcement signal (beta)
▪ 0 if gbest improves
▪ 1 otherwise
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MACROSCOPIC ADAPTIVE PSO (MAPSO)Learning Automaton
CLPSO Population
Learn From Exemplars
Probability Vector
Action Set
PIncrement
PFixed
PDecrement
Increase m
Keep m
Decrease m m
Re
info
rce
me
nt
Sign
al
▪ Microscopic behavior of PSO population
▪ Refreshing gap adjustment by group of LA
▪ Group of Triple action LA
▪ Reinforcement signal (beta)
▪ 0 if pbest improves
▪ 1 otherwise
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MICROSCOPIC ADAPTIVE PSO (MIPSO)
LA Groups
Reinforcement Signal
LA1
CLPSO Population
P1
Inc.
Dec.
Fix.
LAN
Inc.
Dec.
Fix.
m1 m1
PNmN mN
▪ TEC 2006 Benchmark Functions (16)
▪ Unimodal and Simple multimodal (2)
▪ Unrotated multimodal (6)
▪ rotated multimodal (6)
▪ Composition (2)
▪ 10 – Dimensional test
▪ 10 particles
▪ 30000 fitness evaluations
▪ 30 – Dimensional test
▪ 40 particles
▪ 200000 fitness evaluations
▪ Each test runs 30 times
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EXPERIMENTAL SETUP▪ Triple Action LA
▪ Linear Reward – Penalty Algorithm
▪ Alpha = Beta = 0.1
▪ Refreshing gap rang = [1, 20]
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2
0 00
4 4
2
0 0
2
0
1
0 0 00
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Simple Unrotated Rotated Composition
CPSO-H CLPSO MaPSO MiPSO12
EXPERIMENTAL RESULTS (10D)
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0 0 00
3
2
1
0 0
1
00
3 3
1
0
0.5
1
1.5
2
2.5
3
3.5
Simple Unrotated Rotated Composition
CPSO-H CLPSO MaPSO MiPSO13
EXPERIMENTAL RESULTS (30D)
3
2
10
6
2
1
1
7
0 2 4 6 8 10 12 14 16 18
10-D
30-D
CPSO-H CLPSO MaPSO MiPSO14
EXPERIMENTAL ANALYSIS (10D, 30D)
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CONCLUSION▪ Adaptive and Agile Parameter Selection
▪ Maintain Solution Diversity
▪ Balancing local and global searches
▪ Escaping from local minima
▪ MaPSO features
▪ Low dimensional problems
▪ Rotated benchmarks
▪ MiPSO features
▪ Higher dimensional problems
▪ Rotated, Unrotated and Composition problems
FOLLOW US
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WE CAN ONLY SEE A SHORT DISTANCE AHEAD, BUT WE CAN SEE PLENTY THERE THAT NEEDS TO BE DONE.
ALAN TURINGThank you for your attention!17