Discrete Dynamics in Nature and SocietyVolume 2011 (2011), Article ID 171697, 11 pagesdoi:10.1155/2011/171697

Research Article

Identifying a Global Optimizer with Filled Function for Nonlinear Integer Programming

Wei-Xiang Wang,¹ You-Lin Shang,² and Lian-Sheng Zhang³

¹Department of Mathematics, Shanghai Second Polytechnic University, Shanghai 201209, China²Department of Mathematics, Henan University of Science and Technology, Luoyang 471003, China³Department of Mathematics, Shanghai University, Shanghai 200444, China

Received 27 March 2011; Revised 10 July 2011; Accepted 15 July 2011

Academic Editor: Rigoberto Medina

Copyright © 2011 Wei-Xiang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This paper presents a filled function method for finding a global optimizer of integer programming problem. The method contains two phases: the local minimization phase and the filling phase. The goal of the former phase is to identify a local minimizer of the objective function, while the filling phase aims to search for a better initial point for the first phase with the aid of the filled function. A two-parameter filled function is proposed, and its properties are investigated. A corresponding filled function algorithm is established. Numerical experiments on several test problems are performed, and preliminary computational results are reported.

1. Introduction

Consider the following general global nonlinear integer programming: where is a box set and is the set of integer points in . The problem (P) is important since lots of real life applications, such as production planning, supply chains, and finance, are allowed to be formulated into this problem.

One of main issues in the global optimization is to avoid being trapped in the basins surrounding local minimizers. Several global optimization solution strategies have been put forward to tackle with the problem (P). These techniques are usually divided into two classes: stochastic method and deterministic method (see [1–7]). The discrete filled function method is one of the more recently developed global optimization tools for discrete global optimization problems. The first filled function was introduced by Ge and Qin in [8] for continuous global optimization. Papers [6, 7, 9–11] extend this continuous filled function method to solve integer programming problem. Like the continuous filled function method, the discrete filled function method also contains two phases: local minimization and filling. The local minimization phase uses any ordinary discrete descent method to search for a discrete local minimizer of the problem (P), while the filling phase utilizes an auxiliary function called filled function to find a better initial point for the first phase by minimizing the constructed filled function. The definitions of the filled function proposed in the papers [9, 10] are as follows.

Definition 1.1 (see [9]). is called a filled function of at a discrete local minimizer if meets the following conditions. (1) has no discrete local minimizers in the set , except a prefixed point that is a minimizer of .(2)If is not a discrete global minimizer of , then does have a discrete minimizer in the set .

Definition 1.2 (see [10]). is called a filled function of at a discrete local minimizer if meets the following conditions. (1) has no discrete local minimizers in the set , where the prefixed point is not necessarily a local minimizer of .(2)If is not a discrete global minimizer of , then has a discrete minimizer in the set .

Although Definitions 1.1 and 1.2 and the corresponding filled functions proposed in the papers [9, 10] have their own advantages, they have some defects in some degree, for example, as the prefixed point in Definition 1.2 may be a minimizer of the given filled function, which will result in numerical complexity at the iterations or cause the algorithm to fail. To avoid these defects, in this paper, we give a modification of Definitions 1.1 and 1.2 and propose a new filled function.

The rest of this paper is organized as follows. In Section 2, we review some basic concepts of discrete optimization. In Section 3, we propose a discrete filled function and investigate its properties. In Section 4, we state our algorithm and report preliminary numerical results. And, at last, we give our conclusion in Section 5.

2. Basic Knowledge and Some Assumptions

Consider the problem (P). Throughout this paper, we make the following assumptions.

Assumption 2.1. There exists a constant satisfying .

Assumption 2.2. There exists a constant , such that holds, for any , , where is a neighborhood of the point as defined in Definition 2.4.

Most of the existing discrete filled function methods are used for solving a box constrained problem. To an unconstrained global optimization problem if satisfies , then there exists a box set which contains all discrete global minimizers of . Therefore, can be turned into an equivalent formulation in (P) and solved by any discrete filled function method.

For convenience, in the following, we recall some preliminaries which will be used throughout this paper.

Definition 2.3 (see [10]). The set of all feasible directions at is defined by , where ,   is the ith unit vector (the -dimensional vector with the ith component equal to one and all other components equal to zero).

Definition 2.4 (see [10]). For any , the discrete neighborhood of is defined by .

Definition 2.5 (see [10]). A point is called a discrete local minimizer of over if , for all . Furthermore, if , for all , then is called a strict discrete local minimizer of over . If, in addition, , for all , then is called a strict discrete local (global) minimizer of over .

Algorithm 2.6 (discrete local minimization method). (1)Start from an initial point .(2)If is a local minimizer of over , then stop. Otherwise, let (3)Let , and go to Step (2). Let be a local minimizer of the problem (P). The new definition of the filled function of at is given as follows.

Definition 2.7. is called a discrete filled function of at a discrete local minimizer if has the following properties. (1) is a strict discrete local maximizer of over . (2) has no discrete local minimizers in the region (3)If is not a discrete global minimizer of , then does have a discrete minimizer in the region

3. Properties of the Proposed Discrete Filled Function

Let denote the current discrete local minimizer of (P). Based on Definition 2.7, a novel filled function is proposed as follows: where where and are two parameters and satisfies .

The following theorems ensure that is a filled function under some conditions.

Theorem 3.1. If then is a strict local maximizer of .

Proof. Since is a local minimizer of (P), there exists a neighborhood of such that and hold, for any . It follows that By the condition and the fact that the inequality holds for any real number , we have Hence, , which implies that is a strict local maximizer of .

Lemma 3.2. For every , there exists such that .

For the proof of this lemma, see, for example, [6] or [7].

Theorem 3.3. Suppose that . If and , then is not a local minimizer of .

Proof. For any with , by Lemma 3.2, there exists a direction with such that . For this , we consider the following three cases. Case 1 (). In this case, by using the given condition and the fact that the inequality holds for any real number , we have Since and , we have Hence, in this case, is not a local minimizer of .Case 2 ( and ). In this case, we have which means the conclusion is true in this case.Case 3 ( and ). In this case, we have Hence, in this case, is not a local minimizer of .The above discussion implies that is not a discrete local minimizer of .

Theorem 3.4. Assume that is not a global minimizer of , then there exists a minimizer of in .

Proof. Since is not a global minimizer of , there exists such that ; it follows that . On the other hand, by the structure of , we have for any . This shows is a minimizer of .

4. Filled Function Algorithm and Numerical Experiments

Based on the theoretical results in the previous section, the filled function method for (P) is described now as follows.

Algorithm 4.1 (discrete filled function method). (1)Input the lower bound of , namely, . Input an initial point . Let . (2)Starting from an initial point , minimize and obtain the first local minimizer of . Set ,  , and .(3)Set , ,  ,  , and .(4)Set and .(5)If , then use as initial point for discrete local minimization method to find another local minimizer such that . Set , and go to (3).(6)Let . If there exists such that , then use , where , as an initial point for a discrete local minimization method to find another local minimizer such that . Set , and go to (3).(7)Let . If , then go to (10).(8)If there exists such that , then set ,  ,  , and go to (4).(9)Let . If , then set . Otherwise set , and go to (6).(10)If , then set , and go to (4). (11)Set . If , go to (3). Otherwise, the algorithm is incapable of finding a better minimizer starting from the initial points, . The algorithm stops, and is taken as a global minimizer.

The motivation and mechanism behind the algorithm are explained below.

A set of initial points is chosen in Step to minimize the discrete filled function.

Step represents the situation where the current computer-generated initial point for the discrete filled function method satisfies . Therefore, we can further minimize the primal objective function by any discrete local minimization method starting from .

Step aims at selecting a better successor point. If is not empty, then we get a feasible direction which reduce both the objective function value and filled function value. Otherwise, we can get a descent feasible direction which reduce only filled function value.

In the following, we perform the numerical experiments for five test problems using the above proposed filled function algorithm. All the numerical experiments are programmed in MATLAB 7.0.4. The proposed filled function algorithm succeeds in identifying the global minimizers of the test problems. The computational results are summarized in Table 1, and the symbols used are given as follows:PN: the Nth problem.DN: the dimension of objective function of a problem.IN: the number of iteration cycles.TI: the CPU time in seconds for the algorithm to stop.TN: the number of filled function evaluations for the algorithm to stop.FN: the number of objective function evaluations for the algorithm to stop.

Table 1

1
4
3
2.4136
18087
3617

1
4
3
2.3217
17622
3523

1
4
3
2.4252
19556
3798

2
2
5
35.1435
312342
62468

2
2
5
36.2984
326763
65352

2
2
5
36.6879
330835
66167

3
2
5
204.9916
1558825
311765

3
2
5
206.7242
1617823
323564

3
2
5
205.6871
1593561
318712

4
4
53
3598.3893
33991625
6798325

4
4
53
3612.5671
34043270
6808654

4
4
53
3574.3248
33933790
6786758

5
25
2
148.8163
1158671
244196

5
50
2
1084.7239
9234193
1924634

5
100
2
8891.1984
689656591
15316758

6
25
11
164.2165
1521146
306731

6
50
24
1297.7789
11205803
2467864

6
100
50
9045.2396
828917460
17328966

Problem 1. One has This problem has feasible points where 41 of them are discrete local minimizers but only one of those discrete local minimizers is the discrete global minimum solution: with . We used three initial points in our experiment:, , .

Problem 2. One has where This problem has feasible points. More precisely, it has 207 and 2 discrete local minimizers in the interior and the boundary of box , respectively. Nevertheless, it has only one discrete global minimum solution: with . We used three initial points in our experiment: , , .

Problem 3. One has This problem has feasible points and many discrete local minimizers, but it has only one discrete global minimum solution: with . We used three initial points in our experiment:, , .

Problem 4. One has This problem has feasible points and many local minimizers, but it has only one global minimum solution: with . We used three initial points in our experiment:, , .

Problem 5. One has This problem has many local minimizers, but it has only one global minimum solution: with .In this problem, we used initial point in our experiment for , respectively.

Problem 6. One has This problem has many local minimizers, but it has only one global minimum solution: with .In this problem, we used initial point in our experiment for , respectively.

5. Conclusions

We have proposed a new two-parameter filled function and presented a corresponding filled function algorithm for the solution of the box constrained global nonlinear integer programming problem. Numerical experiments are also implemented, and preliminary computational results are reported. Our future work is to generalize the discrete filled function techniques to mixed nonlinear integer global optimization problem.

Acknowledgment

This paper was partially supported by the NNSF of China under Grant No. 10571137 and 10971053.

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