Redirigiendo al acceso original de articulo en 18 segundos...
ARTÍCULO
TITULO

Construction of the fractional-nonlinear optimization method

Lev Raskin    
Oksana Sira    

Resumen

A method for solving the fractional nonlinear optimization problem has been proposed. It is shown that numerous inventory management tasks, on the rational allocation of limited resources, on finding the optimal paths in a graph, on the rational organization of transportation, on control over dynamical systems, as well as other tasks, are reduced exactly to such a problem in cases when the source data of a problem are described in terms of a probability theory or fuzzy math. We have analyzed known methods for solving the fractional nonlinear optimization problems. The most efficient among them is based on the iterative procedure that sequentially improves the original solution to a problem. In this case, every step involves solving the problem of mathematical programming. The method converges if the region of permissible solutions is compact. The obvious disadvantage of the method is the uncontrolled rate of convergence. The current paper has proposed a method to solve the problem, whose concept echoes the known method of fractional-linear optimization. The proposed technique transforms an original problem with a fractional-rational criterion to the typical problem of mathematical programming. The main advantage of the method, as well its difference from known ones, is the fact that the method is implemented using a single-step procedure for obtaining a solution. In this case, the dimensionality of a problem is not a limiting factor. The requirements to a mathematical model of the problem, which narrow the region of possible applications of the devised procedure, imply:1) the components of the objective function must be separable functions;2) the indicators for the power of all nonlinear terms of component functions should be the same.Another important advantage of the method is the possibility of using it to solve the problem on unconditional and conditional optimization. The examples have been considered.

 Artículos similares

       
 
Na Wei, Yuxin Peng, Kunming Lu, Guixing Zhou, Xingtao Guo and Minghui Niu    
The parallel reservoirs in the upper reach of the Hanjiang River are key projects for watershed management, development, and protection. The optimal operation of parallel reservoirs is a multiple-stage, multiple-objective, and multiple-decision attribute... ver más
Revista: Applied Sciences

 
Bocheng Zhao, Mingying Huo, Ze Yu, Naiming Qi and Jianfeng Wang    
In this study, we propose an aerial rendezvous method to facilitate the recovery of unmanned aerial vehicles (UAVs) using carrier aircrafts, which is an important capability for the future use of UAVs. The main contribution of this study is the developme... ver más
Revista: Aerospace

 
Yi Lu, Dongyan Wei and Hong Yuan    
Magnetic positioning is a promising technique for vehicles in Global Navigation Satellite System (GNSS)-denied scenarios. Traditional magnetic positioning methods resolve the position coordinates by calculating the similarity between the measured sequenc... ver más
Revista: Applied Sciences

 
Changping Sun, Mengxia Li, Linying Chen and Pengfei Chen    
Effective utilization of tugboats is the key to safe and efficient transport and service in ports. With the growth of maritime traffic, more and more large seaports show a trend toward becoming super-scale, and are divided into multiple specialized termi... ver más

 
Esra?a Alkafaween, Ahmad Hassanat, Ehab Essa and Samir Elmougy    
The genetic algorithm (GA) is a well-known metaheuristic approach for dealing with complex problems with a wide search space. In genetic algorithms (GAs), the quality of individuals in the initial population is important in determining the final optimal ... ver más
Revista: Applied Sciences