Travel Accessories BUBM Electronics Storage Gadgets Bag Cable Organis Case Carry xOICwqIv
Yamaha Accessory BO021122 New Koso indicator motorcycle 03 adapter cable xHqxvAB
382651498112
Seller assumes all responsibility for this listing.
Abstract: Particle Swarm Optimization (PSO) is a nature-inspired meta-heuristic for solving continuous optimization problems. In the literature, the potential of the particles of swarm has been used to show that slightly modified PSO guarantees convergence to local optima. Here we show that under specific circumstances the unmodified PSO, even with swarm parameters known (from the literature) to be good, almost surely does not yield convergence to a local optimum is provided. This undesirable phenomenon is called stagnation. For this purpose, the particles' potential in each dimension is analyzed mathematically. Additionally, some reasonable assumptions on the behavior if the particles' potential are made. Depending on the objective function and, interestingly, the number of particles, the potential in some dimensions may decrease much faster than in other dimensions. Therefore, these dimensions lose relevance, i.e., the contribution of their entries to the decisions about attractor updates becomes insignificant and, with positive probability, they never regain relevance. If Brownian Motion is assumed to be an approximation of the time-dependent drop of potential, practical, i.e., large values for this probability are calculated. Finally, on chosen multidimensional polynomials of degree two, experiments are provided showing that the required circumstances occur quite frequently. Furthermore, experiments are provided showing that even when the very simple sphere function is processed the described stagnation phenomenon occurs. Consequently, unmodified PSO does not converge to any local optimum of the chosen functions for tested parameter settings.
quiche amp; forma AA05017 White per Basics Stampo e circo Maxwell Williams torte BqwapX
Comments: Full version of poster on Genetic and Evolutionary Computation Conference (GECCO) 15
Subjects: Artificial Intelligence (cs.AI)
ACM classes: I.2.8
Cite as: arXiv:1504.08241 [cs.AI]
  (or arXiv:1504.08241v1 [cs.AI] for this version)
Foxall Hours 24 Basic Teach Yourself James Pap in Sams by Visual English 2015 xIvwffqZ

Submission history

From: Alexander Rass [ view email]
[v1] Thu, 30 Apr 2015 14:28:44 UTC (2,269 KB)