PSO implementation on Hadoop and standalone

1. PSO implementation on Hadoop and standalone

2. Flowchart1 Inner loop split Initial particles (random) Split1 of particles gbest1 Output Total gbest Total gbest … … Inner loop split SplitN of particles gbestN outer loop

3. Flowchart1 Inner loop split Initial particles (random) Split1 of particles gbest1 Output Total gbest Total gbest … … Inner loop split SplitN of particles gbestN outer loop Reduce phase Map phase

4. Flowchart2 Inner loop Pseudo randomly initialize particles Split1 of particles gbest1 Output Total gbest Total gbest … … Inner loop SplitN of particles gbestN outer loop

5. Flowchart2 Inner loop Pseudo randomly initialize particles Split1 of particles gbest1 HDFS Output Total gbest … … Inner loop SplitN of particles gbestN outer loop (feedback total gbest) Map phase

6. Experiment results # of lamps : 12 targets : 5 * 1 light ranks : 16 Differ_rate : 0.0001 # of splits : 4

7. Experiment results # of lamps : 12 targets : 5 * 1 light ranks : 16 Differ_rate : 0.0001 # of splits : 8

8. Experiment results # of lamps : 396 targets : 90 * 33 light ranks : 16 Differ_rate : 0.0001 # of splits : 4

9. Experiment results # of lamps : 396 targets : 90 * 33 light ranks : 16 Differ_rate : 0.0001 # of splits : 8

10. Exchange of particles • Assume that we have an example: # of splits is 4, # of particles in each split is 2500, and we assign 2500/2(ideal) particles to exchange between splits. Under this condition , each split is responsible for emitting(pseudo randomly) 1250/4 particles to exchange. Constraints: # of particles in each split ≥ (# of splits -1)* # of splits *2

11. Exchange of particles ID0[0…103] ID0[104…207] ID0 ID0[208…311] ID1[0…103] ID1[104…207] ID1 ID1[208…311] ID2[0…103] ID2[104…207] ID2 ID2[208…311] ID3[0…103] ID3[104…207] ID3 ID3[208…311] ID3[0…103],ID2[104…207],ID1[208…311] …… ……