Discrete Particle Swarm Optimization
€30-250 EUR
Betalt ved levering
General informations:
Parameters:
• Number of students (s) – 10 students (S1 S2 S3 S4 S5 S6 S7 S8 S9 S10)
• Number of groups (r)
• Number of themes/topics (k) – 3 (k1 k2 k3)
• Relative difficult of each theme (w) - (w1 =0.4, w2=0.3, w3=0,3)
Themes are divided in order that first theme has difficult k1 w1 =0.4 (theme must be known by student), next theme k2 has w2=0.3 and theme k3 ima has w3=0.3.
Applying of PSO:
Population should be: 5m or 10m
Number of iteration: 2000
2 criterias –parameters “m” which are used for PSO example from literatures:
• Result for student on pre-test
• Experience of learning for students (based on student activities for certain theme)
Add third parameter:
• Time for work on pre-test
Target, output should be - collaborative learning group
1. Theme k1: “Base of software projecting ” – Pre-test T1, w1 =0,4
2. Theme k2: „Templates for software projecting“ – Pre-test T2, w2 =0,3
3. Theme k3: „Advanced strategies in software projecting“ – Pre-test T3, w3 =0,3
Results of test (number of points) has to be between 0 and 1
Pre-test T1 ,weight w=0,4, theme: Base of software projecting
There was about 30 scenarios testing with different settings.
- Population volume (SwarmSize): used 2n, 5n and 12n; could be used for test by tutor (there isn’t big difference if you use 10n instead 12n); in our case n=3
- Range for interia weight (InertiaRange): should be tested [0.1; 1.1], [0.4; 0.9], [0.5; 2.5], i [1.0.; 5.0]
- Factors of learning c1 i c2 (SelfAdjustment) i (SocialAdjustment): testing values c1 = c2 = 0.1; c1 = c2 = 0.5; c1 = c2 = 2.0; c1 = c2 = 5, i c1 = 0.7, c2 = 1.5
- In our case we have 3 input parameters, thus nvars=3
- For these 3 parameters we’ve analyzed top and bottom limit and concluded: lb= [0; 1; 0]; ub= [1; 5; 20] – should be checked additionally
Text highlighted in green is output from Matlab.
We should save picture which Matlab provide for each algorithm.
Projekt ID: #27186617
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