A Parameter-Free Filled Function for Unconstrained Global Optimization

The filled function method is an approach for finding a global minimum of multi-dimensional functions. With more and more relevant research, it becomes a promising way used in unconstrained global optimization. Some filled functions with one or two parameters have already been suggested. However, there is no certain criterion to choose a parameter appropriately. In this paper, a parameter-free filled function was proposed. The definition of the original filled function and assumptions of the objective function given by Ge were improved according to the presented parameter-free filled function. The algorithm and numerical results of test functions were reported. Conclusions were drawn in the end. Key words global optimization - filled function method - local minimizer MSC 2000 90C30

Two-parameters quasi-filled function algorithm for nonlinear integer programming

A quasi-filled function for nonlinear integer programming problem is given in this paper. This function contains two parameters which are easily to be chosen. Theoretical properties of the proposed quasi-filled function are investigated. Moreover, we also propose a new solution algorithm using this quasi-filled function to solve nonlinear integer programming problem in this paper. The examples with 2 to 6 variables are tested and computational results indicated the efficiency and reliability of the pro- posed quasi-filled function algorithm.

One-parameter quasi-filled function algorithm for nonlinear integer programming

A definition of the quasi-filled function for nonlinear integer programming problem is given in this paper. A quasi-filled function satisfying our definition is presented. This function contains only one parameter. The properties of the pro- posed quasi-filled function and the method using this quasi-filled function to solve nonlinear integer programming problem are also discussed in this paper. Numerical results indicated the efficiency and reliability of the proposed quasi-filled function algo- rithm.

A One-parameter Filled Function Method for Nonlinear Integer Programming

This paper gives a new definition of the filled function for nonlinear integer programming problem. A filled function satisfying our definition is presented. This function contains only one parameter. The properties of the proposed filled function and the method using this filled function to solve nonlinear integer programming problem are also discussed. Numerical results indicate the efficiency and reliability of the proposed filled function algorithm.

A New Filled Function for Global Optimization Problems with Box Constraints

In this paper, auxiliary function method for global optimization with box constraints is considered. First, a new non-parameter filled function which has the same local minimizers of the objective function is proposed. By the character that having same local minimizers, and these minimizers are all better than the current minimizer of the objective function, it does not need to minimize the objective function except for thefirst iteration in the filled function method. It changes the frame of conventional filled function methods that objective function and filled function are minimized alternately,and can effectively reduce the iterations of the algorithm and accelerate the speed of global optimization. And then the theoretical properties of the filled function are discussed and the corresponding algorithm is established. Finally, numerical experiments are made and comparisons on several test problems are shown which exhibit the feasibility and effectiveness of the algorithm.

A Filled Function with Adjustable Parameters for Unconstrained Global Optimization

A filled function with adjustable parameters is suggested in this paper for finding a global minimum point of a general class of nonlinear programming problems with a bounded and closed domain. This function has two adjustable parameters. We will discuss the properties of the proposed filled function. Conditions on this function and on the values of parameters are given so that the constructed function has the desired properties of traditional filled function.

A Novel Parameter-Free Filled Function and Its Application in Least Square Method

The filled function algorithm is an important method to solve global optimization problems.In this paper,a parameter-free filled function is proposed for solving general global optimization problem,discuss the theoretical properties of this function and give the corresponding algorithm.The numerical experiments on some typical test problems using the algorithm and the numerical results show that the algorithm is effective.Applying the filled function method to the parameter solving problem in the logical population growth model,and then can be effectively applied to Chinese population prediction.The experimental results show that the algorithm has good practicability in practical application.

A New Non-Parameter Filled Function for Global Optimization Problems

In the paper,to solve the global optimization problems,we propose a novel parameter-free filled function.Based on the non-parameter filled function,a new filled function algorithm is designed.In the algorithm,the selection and adjustment of parameters can be ignored by the characteristic that the filled function is parameter-free.In addition,in the region lower than the current local minimizer of the objective function,the filled function is continuously differentiable which enables any gradient descent method to be used as a local search method in the algorithm.Through numerical experiments by solving two test problems,the effectiveness of the algorithm is verified.

FILLED FUNCTIONS FOR UNCONSTRAINED GLOBAL OPTIMIZATION

The paper is concerned with the filled functions for global optimization of a continuous function of several variables.More general forms of filled functions are presented for smooth and nonsmooth optimizations.These functions have either two adjustable parameters or one adjustable parameter.Conditions on functions and on the values of parameters are given so that the constructed functions are desired filled functions.

New Tunnel-Filled Function Method for Discrete Global Optimization

In this paper,a new transformation function was proposed for finding global minimizer of discrete optimization problems.We proved that under some general assumptions the new transformation function possesses the properties of both the tunneling functions and the filled functions.Only one parameter was included in the proposed function,and it can be adjusted easily in the realization.Numerical results demonstrate the effectiveness of the proposed method.

Unification of Filled Function and Tunnelling Function in Global Optimization

In this paper, two auxiliary functions for global optimization are proposed. These two auxiliary functions possess all characters of tunnelling functions and filled functions under certain general assumptions. Thus, they can be considered as the unification of filled function and tunnelling function. Moreover, the process of tunneling or filling for global optimization can be unified as the minimization of such auxiliary functions. Result of numerical experiments shows that such two auxiliary functions are effective.

A NEW FILLED FUNCTION METHOD FOR INTEGER PROGRAMMING

The Filled Function Method is a class of effective algorithms for continuous global optimization. In this paper, a new filled function method is introduced and used to solve integer programming. Firstly, some basic definitions of discrete optimization are given. Then an algorithm and the implementation of this algorithm on several test problems are showed. The computational results show the algorithm is effective.

A ONE-PARAMETER FILLED FUNCTION FOR NONSMOOTH GLOBAL OPTIMIZATION AND ITS APPLICATION

This paper presents a modified definition of the filled function for finding a global minimizer of a nonsmooth function on a closed bounded set, and then give a one-parameter filled function. Theoretical and numerical properties of the proposed filled function are investigated and a corresponding solution algorithm is proposed. The proposed filled function＇s parameter is easier to be appropriately chosen than previous functions in literatures. Numerical results obtained indicate the efficiency of the proposed filled function method. An improved fingerprint recognition method using global filled function is also reported.

Discrete Global Optimization Problems with a Modified Discrete Filled Function

This paper considers discrete global optimization problems.The traditional definition of the discrete filled function is modified in this paper.Based on the modified definition,a new discrete filled function is presented and an algorithm for discrete global optimization is developed from the discrete filled function.Numerical experiments reported in this paper on several test problems with up to 200 variables have demonstrated the efficiency of the algorithm.

A Class of Parameter-free Filled Functions for Box-constrained System of Nonlinear Equations

In this paper, a class of parameter-free filled functions is proposed for solving box-constrained system of nonlinear equations. Firstly, the original problem is converted into an equivalent global optimization problem. Subsequently, a class of parameter-free filled functions is proposed for solving the problem. Some properties of the new class of filled functions are studied and discussed. Finally, an algorithm which neither computes nor explicitly approximates gradients during minimizing the filled functions is presented. The global convergence of the algorithm is also established. The implementation of the algorithm on several test problems is reported with satisfactory numerical results.

Improved Particle Swarm Optimization for Parameter Identification of Permanent Magnet Synchronous Motor

In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.

A New F-C Function for Box Constrained Global Optimization

To solve the global optimization problems which have several local minimizers,a new F-C function is proposes by combining a lled function and a cross function.The properties of the F-C function are discussed and the corresponding algorithm is given in this paper.F-C function has the same local minimizers with the objective function.Therefore,the F-C function method only needs to minimize the objective function once in the rst iteration.Numerical experiments are performed and the results show that the proposed method is very effective.

Novel Global Optimization Algorithm with a Space-Filling Curve and Integral Function

In this study,we consider the global optimization problem in a hypercube.We use a class of series to construct a curve in a hypercube,which can fill the hypercube,and we present an integral function on the curve.Based on the integral function,we propose an algorithm for solving the global optimization problem.Then,we perform a convergence analysis and numerical experiments to demonstrate the effectiveness of the proposed algorithm.

An Approximate Algorithm for a Class of Nonlinear Bilevel Integer Programming

The algorithm for a class of nonlinear bilevel integer programming is discussed in this paper. It is based on the theory and algorithm for nonlinear integer programming. The continuity methods for integer programming are studied in this paper. After simulated annealing algorithm is applied to the upper-level programming problem and the thought of filled function method for continuous global optimization is applied to the corresponding lower-level programming, an approximate algorithm is established. The satisfactory algorithm is elaborated in the following example.

Isoperimetry of nilpotent groups

This survey gives an overview of the isoperimetric properties of nilpotent groups and Lie groups. It discusses results for Dehn functions and filling functions as well as the techniques used to retrieve them. The content reaches from long standing results up to the most recent development.