Effectiveness and Efficiency of Particle Swarm Optimization Technique in Inverse Heat Conduction Analysis
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3. Inverse Heat Transfer Problem Finding Surface Values from Readings of Thermocouples Inside the Plate Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
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9. Update Equations i ’s best performance x i p g p i v i overall best performance Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
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11. Test Case I: 1D Transient Problem without regularization with regularization Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
12. Test Case II: 2D Problem Axisymmetric around the left side Similar to a thermocouple hole inside a plate Top surface is subjected to a heat flux similar to those happening in real cooling of hot steel Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
13. Classical Approach & Large Time Step Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
14. Classical Approach & Small Time Step Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
15. PSO Is Stable for Small Time Steps Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
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17. t – Test Results critical t -value = 1.73 If t -value > 1.73, Method 2 performs better than Method 1 in at least 95% of cases GA : Genetic Algorithm Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion CRPSO RPSO 6 CRPSO PSO 5 RPSO PSO 4 CRPSO GA 3 RPSO GA 2 PSO GA 1 Method 2 Method 1 Test # 7.75 3.45 3.04 Test 6 7.92 7.59 4.63 Test 5 1.27 3.32 1.49 Test 4 13.41 17.76 10.78 Test 3 9.41 12.52 6.60 Test 2 8.26 10.08 4.88 Test 1 Test Case 3 Test Case 2 Test Case 1
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20. Effect of Self-Confidence Parameter (1) Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
21. Effect of Self-Confidence Parameter (1) Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
22. Effect of Self-Confidence Parameter (2) Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
23. Effect of Self-Confidence Parameter (3) Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
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29. What do we get from our experiments? Background > Inverse Problem > Particle Swarm Optimization > Results > Conclusion > Future Work
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32. Improved Objective Function Regularization Penalizing nonphysical oscillations in the results Background > Inverse Problem > Particle Swarm Optimization > Results > Conclusion > Future Work q i : Heat Flux Component
33. World Crude Steel Production Background > Inverse Problem > Particle Swarm Optimization > Results > Conclusion > Future Work "World Steel in Figures, 2007", http://www.worldsteel.org/
34. Experimental Setup at UBC Background > Inverse Problem > Particle Swarm Optimization > Results > Conclusion > Future Work
40. Statistical t – Test A significance level of 5%, and a 10+10-2=18 degrees of freedom a critical t -value of 1.73 Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
41. Effect of the Regularization Parameter (1) Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
42. Effect of the Regularization Parameter (2) Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
43. Effect of the Regularization Parameter (3) Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
44. Effect of Using PSO Variants Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
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46. PSO Is Stable for Small Time Steps Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
47. Test Case III: 3D Steady Problem Background > Classical Methods > Particle Swarm Optimization > Test Cases and Results > Efficiency > Effectiveness > Conclusion
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Editor's Notes
James Kennedy (social psychologist) / Eberhart: Professor of Electrical and Computer Engineering / Survival
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