ANN Training with PSO
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From: Tricia Rambharose on 3 Aug 2010 18:54 Good days, I realised the fix. Now it works by stopping at the specified ANN goal! What I still need assistance on is.. " at the minima point the PSO continues for a lot of iterations even though all particles continuously give the same fitness value. This is a waste of time since no change is observed in particles positions for quite a while before it stops. " and > Can you differentiate true_global_minimum and thresh_for_succ and also provide any explanation for this stagnation on PSO? Thanks!
From: George Evers on 5 Aug 2010 01:48
Tricia, The true global minimum, "true_global_minimum," is set in section “(6) Objectives” to enable any trial that successfully minimizes the fitness value to its lowest possible value to terminate rather than spending excess time in computation. Once the number of significant figures carried internally by MATLAB agree without error with the specified "true_global_minimum," no further optimization is possible. Of course, when many decision variables map through a complex formula to one fitness value, the decision variables will not be optimized to the same number of decimal places as the fitness value itself. When switch "OnOff_SuccessfulUnsuccessful" is active in subsection "REGPSO & PSO Success Measures" of the first section, the concurrent activation of switch "OnOff_Terminate_Upon_Success" causes each trial to terminate when the threshold for success, "thresh_for_succ," is achieved as specified in Objectives.m. This will stop PSO when the subjective threshold required for a trial to be considered successful is reached. As for stopping execution if PSO stagnates before reaching the specified threshold for success, Van den Bergh's normalized swarm radius criterion terminates the search when all particles are near the global best, which is a good indication of stagnation [1]. According to quick empirical testing done during thesis, monitoring particles' proximities to detect stagnation is more efficient than monitoring the number of iterations for which the function value fails to improve; this stands to reason since monitoring the source of a problem is more efficient than waiting for an undesirable side effect to occur, which is also the logic behind preventive medicine and firewalls. Switch "OnOff_NormR_stag_det" can be activated in subsection "Termination Criteria for REGPSO & PSO Algorithms" in the first section. At this point, RegPSO goes a step farther than terminating the search by regrouping the swarm about the global best for continued progress when stagnation is detected, which allows the function value to improve over multiple groupings. RegPSO can be activated via switch "OnOff_RegPSO" in subsection "PSO Algorithm Selection" of the first section. I've improved Appendix A of the documentation in response to your questions [2]. Thank you, and keep them coming. Cordially, George [1] F. van den Bergh, “An Analysis of Particle Swarm Optimizers,” PhD thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa, 2002 [2] http://www.georgeevers.org/pso_research_toolbox_documentation.doc |