Colliding bodies optimization (CBO) is a new population-based stochastic optimization algorithm based on the governing laws of one dimensional collision between two bodies from the physics. Each agent is modeled as a body with a specified mass and velocity. A collision occurs between pairs of objects to find the global or near-global solutions. Enhanced colliding bodies optimization (ECBO) uses...

Large stock price movements are modeled as jumps in the stochastic processes of stock prices. In the current literature, the jump intensity is typically specified in models as a function of the current diffusive volatility and past jump intensities, while the jump size is assumed to be independent of the jump intensity. We use a nonparametric jump detection test to identify jumps in several sto...

In this paper we propose some novel planning and routing strategies for Dubins’ vehicle, i.e., for a nonholonomic vehicle moving along paths with bounded curvature, without reversing direction. First, we study a stochastic version of the Traveling Salesperson Problem (TSP): given n targets randomly sampled from a uniform distribution in a rectangle, what is the shortest Dubins’ tour through the...

Many stochastic approximation procedures result in a stochastic algorithm of the form 1 hk+l = h k + (bk A k h k ) , k for all IC = 1 , 2 , 3 . . . . . Here, { b k , IC = 1,2 ,3 , . . .} is a Rd-valued process, { Ak , IC = 1 , 2 . 3 . . . . } is a symmetric, positive semidefinite Redxd-valued process, and { h k k = 1,2 ,3 , . .} is a sequence of stochastic estimates which hopefully converges to...

The precise relationships that exist between verbal working memory and language processing remain underdetermined. We argue that while neuroimaging methods have the potential to provide important insights into the links between these two cognitive domains, an over-reliance on a single theoretical perspective has stunted progress toward this end. Specifically, neuroimaging studies in the domain ...

The members of a sequence of jointly independent random variables X[n] have probability density functions of the following form. f X (x; n) = 1 − 1 n 1 √ 2πσ exp − 1 2σ 2 x − n − 1 n σ 2 + 1 n σ exp(−σx)u(x) Determine whether or not the random sequence X[n] converges in (a) the mean-square sense, (b) probability, (c) distribution. Recall, from Definition 6.7-5 on page 379 in [4], that a random ...