In this paper, monkey algorithm (MA) is designed to solve global numerical optimization problems with continuous variables. The algorithm mainly consists of climb process, watch-jump process, and somersault process in which the climb process is employed to search the local optimal solution, the watch-jump process to look for other points whose objective values exceed those of the current soluti...

A globally adaptive algorithm for numerical multiple integration over an n-dimensional simplex is described. The algorithm is based on a subdivision strategy that chooses for subdivision at each stage the subregion (of the input simplex) with the largest estimated error. This subregion is divided in half by bisecting an edge. The edge is chosen using information about the smoothness of the inte...

facility location-allocation models are used in a widespread variety of applications to determine the number of required facility along with the relevant allocation process. in this paper, a new mathematical model for the capacitated multi-facility location-allocation problem with probabilistic customer's locations and fuzzy customer’s demands under the hurwicz criterion is proposed. ...

fuzzy time series have been developed during the last decade to improve the forecast accuracy. many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. in this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effici...

This study is devoted to discrete sizing optimization of truss structures employing an efficient discrete evolutionary meta-heuristic algorithm which uses the Newton gradient-based method as its updating scheme and it is named here as Newton Meta-heuristic Algorithm (NMA). In order to enable the NMA population-based meta-heuristic to effectively explore the discrete design space, a term contain...

Given a black box for f, a smooth real scalar function of d real variables, one wants to estimate [symbol: see text]f at a given point with n bits of precision. On a classical computer this requires a minimum of d + 1 black box queries, whereas on a quantum computer it requires only one query regardless of d. The number of bits of precision to which f must be evaluated matches the classical req...

The Metropolis algorithm [8] is a mainstay of scientific computing. Indeed it appears first on a list of the “Top Ten Algorithms” [12]. It gives a method for sampling from probability distributions on high-dimensional spaces when these distributions are only known up to a normalizing constant. For background and references to extensive applications in physics, chemistry, biology and statistics,...

To efficiently implement implicit Runge-Kutta (IRK) methods for solving large scale stiff ordinary differential equation systems, this paper proposes a new numerical algorithm, which contains a new modified Newton iterative method for solving the nonlinear stage equations of IRK, and two new rules for controlling the updating of Jacobian matrices. A convergence analysis shows that the new modif...

High-energy physics experiments rely on reconstruction of the trajectories of particles produced at the interaction point. This is a challenging task, especially in the high track multiplicity environment generated by p-p collisions at the LHC energies. A typical event includes hundreds of signal examples (interesting decays) and a significant amount of noise (uninteresting examples). This work...

In this paper, a computational model of fuzzy reliability focusing on solving the engineering problems with random general stress–fuzzy general strength is presented. The mathematical basis of this computational model is that the fuzzy probability can be computed with the computational method of conventional probability by use of a mathematical transition. Based on this computational model, a n...