This work presents a genetic algorithm (GA)-based optimization technique, called GA-ParFnt, to find the Pareto frontier for optimizing data transfer versus job execution time in grids. As the performance of a generic GA is not suitable to find such Pareto relationship, major modifications are applied to it so that it can efficiently discover such relationship. The frontier curve representing th...

An easy-to-use procedure is presented for improving the ε-constraint method for computing the efficient frontier of the portfolio selection problem endowed with additional cardinality and semicontinuous variable constraints. The proposedmethod provides not only a numerical plotting of the frontier but also an analytical description of it, including the explicit equations of the arcs of parabola...

Simulation is a powerful tool for modeling complex systems with intricate relationships between various entities and resources. Simulation optimization refers to methods that search the design space (i.e., the set of all feasible system configurations) to find a system configuration (also called a design point) that gives the best performance. Since simulation is often time consuming, sampling ...

This paper explores the use of unmanned aerial vehicles (UAVs) in wildfire monitoring. To begin establishing effective methods for autonomous monitoring, a simulation (FLAME) is developed for algorithm testing. To simulate a wildfire, the well established FARSITE fire simulator is used to generate realistic fire behavior models. FARSITE is a wildfire simulator that is used in the field by Incid...

Among today’s rich economies, per capita output levels diverged before converging to the levels of the frontier economy. Since frontier economies grew at stable rates, non-frontier economies display an S-shaped transition path. Along this transition, there are "catapult effects": longer episodes of stagnation are associated with faster subsequent rates of convergence to the frontier. Many of to...

Support Vector Machines are models widely used in supervised classification. The classical model minimizes a compromise between the structural risk and empirical risk. In this paper, we consider Machine with feature selection design implement bi-objective evolutionary algorithm for approximating Pareto optimal frontier of two objectives. metaheuristic is based on non-dominated sorting genetic i...

   Evaluate the performance of companies on the Stock Exchange using non-parametric methods is very important. DEA and DEA-R with the strategies for piecewise linear frontier production function and use of available data, assess the stock company. In this study, using a neural network algorithm DEA and DEA-R is suggested to classify the first companies in the stock exchange; Secondly, using the...

The conventional Cobb-Douglas production function model require strong assumptions such as the constant elasticity and the entire frontier efficiency. The varying elasticity production function is an useful extension but it does not take frontier efficiency into account, and the data envelope analysis model involves the use of linear programming methods to estimate the frontier efficiency where...

We present an advanced Bidirectional A* algorithm featuring an application of Frontier Search and a strategy for the performance-efficient utilization of External Memory. We present the results of an experimental evaluation demonstrating that this algorithm is capable of tackling exceptionally large state spaces while consuming significantly less time and space than its A* counterpart. For inst...