1. Parallelization of a Swarm Intelligence System
Iman Eshraghi
School of Computer Science
University of Ottawa, Ottawa, Canada

2. Contents Introduction Problem Statement Related work ABC Algorithm
Methodology
Parallelization
Results
Conclusion
References

3. Introduction Swarm Intelligence Artificial / Natural Systems Agents
Boids

4. Introduction SI concepts: self organizing labor division Feedback
Interaction
labor division
Simultaneous task performance
Response to the environment

5. Introduction Optimization (problems – algorithms)
:: discrete variables -> combinatorial optimization problems
:: continuous variable -> multimodal problems,..
Ackley’s Mulitmodal Function

6. Optimization (algorithms) Heuristic vs. Meta-heuristic
Introduction
Optimization (algorithms)
Heuristic vs. Meta-heuristic

7. Meta-heuristic Optimization Techniques
Introduction
Meta-heuristic Optimization Techniques

8. Problem Statement - Hyphothesis - Goal
Metaheuristic optimization technique of choice not optimal
- Goal
Optimizing the metaheuristic optimization technique
Presenting a parallel version of the algorithm for shared memory architecture

9. Related Work
Parallelization of the algorithm by assigning different threads to separate swarms
Parallelization strategies for a distributed memory multiprocessor architecture under the Message Passing Interface (MPI)
CUDA-based Bees Algorithm called CUBA

10. ABC Proposed by Dervis Karaboga in 2005 Why ABC? Applications:
Artificial networks
Graph Theory
TSP
Leaf-constrained Spanning Trees
Power loss Minimization in Distributed networks …

11. ABC Definitions: Food Source Bees Dancing Area Employed Unemployed
Scouts
Onlookers
Dancing Area

12. Algorithm (pseudo-code)
ABC
Algorithm (pseudo-code)

13. Movement of Onlookers: Movement of Scouts:
ABC
Movement of Onlookers:
Movement of Scouts:

14. Methodology
Theory

15. Matlab Parallel Computing Toolbox
Parallelization
Matlab Parallel Computing Toolbox

16. SPMD (Single Program Multiple Data)
Parallelization
Flynn's taxonomy
Single instruction stream
Multiple instruction streams
Single program
Multiple programs
Single data stream
SISD
MISD
Multiple data streams
SIMD
MIMD
SPMD
MPMD
SPMD
(Single Program Multiple Data)

17. spmd numlabs labindex labSend(data, workerId) labReceive(workerId)
Parallelization
spmd
numlabs
labindex
labSend(data, workerId)
labReceive(workerId)

18. Parallelization
An spmd program needs workers to cooperate on the program
-matlabpool open local 4
The client and the workers share a single program in which some commands are delimited within blocks opening with spmd and closing with end.
The client executes commands up to the first spmd block, when it pauses, the workers execute the code in the block. Once they finish, the client resumes execution.

19. Parallelization
1. Generate SN initial solutions randomly and evaluate them. Place them in the shared memory S
2. Divide the solutions equally among p processors by copying SNp solutions to the local memory of each processor
3. Steps 4 to 10 are carried out in parallel at each processor Pr
4. For each solution Xi in the local memory Mr of processor Pr, determine a neighbor Vi using (4) and perform a greedy selection between Xi and Vi
5. Calculate the probabilities for the solutions in Mr using (5)
6. Use the roulette wheel selection method to place the onlookers on the food sources in Mr and improve the corresponding solutions (as in step 4)
7. Determine the abandoned solution (if any) in Mr and replace it with a new randomly produced solution.
8. Record the best local solution obtained till now at Pr
9. Copy the solutions in Mr to the corresponding slots in S
10. Repeat steps 4 to 9 until MCN cycles are completed
11. Determine the global best solution among the best local solutions recorded at each processor.

20. Parallelization spmd SNp=SourceNum/numlabs;
a = ( labindex - 1 ) *SNp+1;
b =SNp* labindex;
S_p_food=food(a:b);
[min_value,index]=min([S_p_food.fit]);
BestSource=S_p_food(index);%Global food .
Employed Search Step
Onlooker Search Step
Scout Search Step
end

21. Comparison Serial: Parallel:
Results
Comparison
Serial:
Parallel:

22. Results
Serial Parallel

23. Results
Speed up => / = 1.9 x

24. Conclusion
The applied parallelization technique for ABC algorithm has lead to speed up.
The quality of results are not necessarily improved or the opposite in this technique.
Future Work:
Extending the Algorithm on the Cluster through Matlab Distributed Computing Server

25. References
Harikrishna Narasimhan. Parallel artifcial bee colony (pabc) algorithm World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009.
Fahad S. Abu-Mouti and Mohamed E. El-Hawary. Overview of artifcial bee colony (abc) algorithm and its applications IEEE International Systems Conference SysCon2012, Mar 2012.
Guo-Heng Luo, Sheng-Kai Huang, Yue-Shan Chang, and Shyan-Ming Yuan. A parallel bees algorithm implementation on GPU. Journal of Systems Architecture, 60(3): , Mar 2014.
Dusan Ramljakb Milica Selmicc & Dusan Teodorovicc Tatjana Davidovica, Tatjana Jak-sica. Parallelization strategies for bee colony optimization based on message passing communication protocol. Optimization, 62(8): , Aug 2013.
Matlab, Mathworks: ,
Wikipedia: iple_programs.2C_multiple_data_streams.29

26. Questions
Q 1:
What are some of the difference between heuristic and metaheuristic algorithms?
Q 2:
Can you name some of the algorithms in each of the above-mentioned categories?
Q 3:
Can we predict whether parallelization of optimization algorithms will achieve a speed up?