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#### Presentation Transcript

**1. **Optimization Using Particle Swarm with Near Neighbor Interactions
Kalyan Veeramachaneni
Thanmaya Peram
Chilukuri K Mohan
Lisa Ann Osadciw

**2. **Overview Introduction
Motivation
Fitness- Distance Ratio
FDR-PSO Algorithm
Particle Dynamics
Experimental Settings
Results and Analysis
Related Work
Summary

**3. **Introduction : Particle Swarm Optimization Inspired from social impact theory
Each particle influenced by its own previous experience, “pbest”
Also influenced by local best in neighborhood, “lbest”
Simulation results show that complete graph topology yields better results than other topologies

**4. **Motivation Problems with PSO execution
Premature convergence
Clustering of particles
Goal : To overcome these problems, exploiting “social impact theory”

**5. **Fitness-Distance Ratio Evaluating influence of jth particle on the ith particle (along the dth dimension)
where Pj is the previous best position visited by the jth particle
Xi is the position of the particle under consideration

**6. **FDR-PSO Algorithm Each particle influenced by
Its own previous best (pbest)
Global best particle (gbest)
Particle that maximizes FDR (nbest)
Velocity Update Equation
Position Update Equation

**7. **FDR-PSO Algorithm Algorithm FDR-PSO:
For t= 1 to the max. bound on the number of generations,
For i=1 to the population size,
Find gbest;
For d=1 to the problem dimensionality,
Find nbest which maximizes the FDR;
Apply the velocity update equation;
Update Position;
End- for-d;
Compute fitness of the particle;
If needed, update historical information regarding Pi and Pg;
End-for-i;
Terminate if Pg meets problem requirements;
End-for-t;
End algorithm.

**8. **Particle Dynamics – I Different nearest best neighbors for a particle along different dimensions
Nearest best neighbor often poorer than global best
Possible overlap between gbest or pbest and nbest for small populations
Overlap of 40% found in a population size of 10

**9. **Particle Dynamics -II
Greater exploration avoiding premature convergence
Increased Population Diversity

**10. **Particle Dynamics -II

**11. **Particle Dynamics – II

**12. **Experimental Settings - I Experimental Settings

**13. **Experimental Settings - II FDR-PSO parameters
Notation
?1, ?2, ?3 represent the weights given to pbest, gbest and nbest terms respectively
Variations of FDR-PSO are obtained by varying the three weights
PSO parameter selection
Equal social and Cognitive learning rates

**14. **Results and Analysis -I

**15. **Results and Analysis -II

**16. **Summary of Results

**17. **Results and Analysis – III FDR-PSO variations consistently outperformed PSO
FDR-PSO(112) was the best performer
nbest term is more important for multimodal functions

**18. **Performance of FDR-PSO Variations FDR-PSO(112) and FDR-PSO(012) outperform PSO on 5 out of 6 benchmark problems
FDR-PSO(102) outperform FDR-PSO(111) in 4 out of 6 benchmark problems
FDR-PSO(002) outperforms FDR-PSO(111) and PSO in 3 out of 6 benchmark problems

**19. **Related Work ARPSO: Diversity measurement makes the algorithm alternate between attraction and repulsion phases
PSO with mass extinction (HPSO) : Velocities are reinitialized after each extinction interval
Hybrid PSO : Population is split into subpopulations and PSO algorithm is hybridized with features from genetic algorithms

**20. **FDR-PSO Vs Other Variations -I

**21. **FDR-PSO Vs Other Variations -II Many PSO variations introduce additional control parameters which are not easy to determine
FDR-PSO achieves better minima without any additional parameters
Other variations are extrinsic to particle dynamics, and hence can be applied to FDR-PSO as well

**22. **Summary Designed a new algorithm which partly follows “social impact theory”
Modeled the “Fitness-Distance Ratio” metric
Improved performance compared to PSO and its previous variations
Significantly less affected than PSO by problems such as premature convergence, loss of diversity in population

**23. **Development and Research in Evolutionary Algorithms for Multisensor Smart Networks
DreamsNet
277, Link Hall
Syracuse University
Syracuse, NY –13244