In this paper, we give a cross-disciplinary survey on “population-based” Monte Carlo algorithms. These algorithms consist of a set of “walkers” or “particles” for the representation of a high-dimensional vector and the computation is carried out by a random walk and split/deletion of these objects. The algorithms are developed in various fields in physics and statistical sciences and called by ...

In this paper, the pricing of a European call option on the underlying asset is performed by using a Monte Carlo method, one of the powerful simulation methods, where the price development of the asset is simulated and value of the claim is computed in terms of an expected value. The proposed approach, applied in Monte Carlo simulation, is based on the Black-Scholes equation which generally def...

We give a cross-disciplinary survey on “population” Monte Carlo algorithms. In these algorithms, a set of “walkers” or “particles” is used as a representation of a high-dimensional vector. The computation is carried out by a random walk and split/deletion of these objects. The algorithms are developed in various fields in physics and statistical sciences and called by lots of different terms – ...

This paper presents an evolutionary real options model of optimization by genetic algorithms and Monte Carlo simulation, to select the best maritime domain protection system configuration and a corresponding adaptive plan with the right amount of flexibilities to address uncertainty. Monte Carlo simulations enumerate diverse terrorism threat paths, decision trees identify possible system (re)co...

A scheme for the scheduling of Flexible Manufacturing Systems (FMS) has been developed which integrates neural networks, parallel Monte-Carlo simulation, genetic algorithms. and machine learning. Modular neural networks are used to generate a small set of atlractive plans and schedules from a larger list of such plans and schedules. Parallel Monte-Carlo Simulation predicts rhe impact of each on...

introduction: in recent decades, several monte carlo codes have been introduced for research and medical applications. these methods provide both accurate and detailed calculation of particle transport from linear accelerators. the main drawback of monte carlo techniques is the extremely long computing time that is required in order to obtain a dose distribution with good statistical accuracy. ...

We present the results obtained from an objective search for stellar clusters, both in the currently active nuclear starburst region, and in the post-starburst disk of M82. Images obtained with the Advanced Camera for Surveys (ACS) aboard the Hubble Space Telescope (HST) in F435W(B), F555W(V), and F814W(I) filters were used in the search for the clusters. We detected 653 clusters of which 393 a...

introduction : due to the importance of laser light penetration and propagation in biological tissues, many researchers have proposed several numerical methods such as monte carlo, finite element and green function methods. among them, the monte carlo method is an accurate method which can be applied for different tissues. however, because of its statistical nature, monte carlo simulation requi...

in this paper, we develop a capacitated location-covering model considering interval values for demand and service parameters. we also consider flexibility on distance standard for covering demand nodes by the servers. we use the satisfaction degree to represent the constraint of service capacity. the proposed model belongs to the class of mixed integer programming models. our model can be redu...

monte carlo methods designed base on stochastic reiterations and can be useful in uncertainty conditions. the purpose of this research was investigation of uncertainty to rank desertification indicators. in order to decrease uncertainty have been applied monte carlo reiterations for each value of indicator, if rank of xi is higher than rank of xk constantly. using multi dimension integrals the ...