Genetic and Evolutionary Computing

To overcome the drawbacks of artificial bee colony(ABC) algorithm that converges slowly in the process of searching and easily suffers from premature, this paper presents an effective approach, called ABC using opposition-based learning(OBL-ABC). It generates opposite solution by the employed bee and onlooker bee, and chooses the better solution as the new locations of employed bee and onlooker bee according to the greedy selection strategy in order to enlarge the search areas; the new approach proposes a new update rule which can retain the advantages of employed bee and onlooker bee and improve the exploration of OBL-ABC. Experiments are conducted on a set of test functions to verify the performance of OBL-ABC, the results demonstrate promising performance of our method OBL-ABC on convergence and it is suitable for solving the optimization of complex functions.

Operator equation and the fixed point problem are an important component of nonlinear functional analysis theory. They are playing important role in solving nature and uniqueness problems about all kinds of differential equations and integral equations. Generally, the monotone operator has been defined with compactness, continuity and concavity and convexity in partially ordered Banach space. In this paper, without compactness and continuity, concavity and convexity of functions, a new fixed point theorem of increasing and decreasing operator and mixed monotone operator has obtained through introducing order-difference in the cone.

This work investigates a bio-inspired adaptive sampling immune optimization algorithm to solve linear or nonlinear chance-constrained optimization problems without any noisy information. In this optimizer, an efficient adaptive sampling detection scheme is developed to detect individual’s feasibility, while those high-quality individuals in the current population can be decided based on the reported sample-allocation scheme; a clonal selection-based time-varying evolving mechanism is established to ensure the evolving population strong population diversity and noisy suppression as well as rapidly moving toward the desired region. The comparative experiments show that the proposed algorithm can effectively solve multi-modal chance-constrained programming problems with high efficiency.

Based on the theory of project management and human resources, combining with the practical construction situations in China, using theoretical analysis and comparative analysis, four important characteristics are extracted in engineering project using EPC mode, that is, mobility, duality, commodity & humanity and collaboration. Furthermore, it is analyzed the origins and implications of above characteristics in depth, and some management methods are presented with the guidance in connection with them in theory.

In this paper, a communication strategy for hybrid Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.

Rapidly development of the cloud computing and Internet makes load balance technique become more and more significant to us than ever. A perfect scheduling algorithm is the key to solve the load balance problems which can not only balance the load, but also can meet the users’ needs. An optimal load balance algorithm is proposed in this paper. Algorithm proposed in this paper can enhance production of the systems and schedule the tasks to virtual machines (VMs) more efficiently. Finishing time of all tasks in the same system will be less than others’. The simulation tools is the CloudSim.

A command center is any place that is used to provide centralized command for some purpose. A command center enables an organization to function as designed, to perform day-to-day operations regardless of what is happening around it. There are parts of being human because no one acts perfectly correctly all the time. It’s same with command center, we called it operation miss. We use RCA when it is released. Root Cause Analysis (RCA) is a problem-solving method used to identify and prove causes, design actions, and assess results. RCA is performed for pervasive issues that are identified through either persistent gaps or pervasive gaps.

Cat Swarm Optimization (CSO) is a new swarm intelligence based algorithm, which simulates the behaviors of cats. In CSO, there are two search modes including seeking and tracing. For each cat (solution) in the swarm, its search mode is determined by a parameter

MR

(mixture ratio). In this paper, we propose a new CSO algorithm by dynamically adjusting the parameter

MR

. In addition, a Cauchy mutation operator is utilized to enhance the global search ability. To verify the performance of the new approach, a set of twelve benchmark functions are tested. Experimental results show that the new algorithm performs better than the original CSO algorithm.

In this paper, we proposed a new multi-objective optimization algorithm named Nondominated Sorting Invasive Weed Optimization (NSIWO) which was inspired from Nondominated Sorting Genetic Algorithm II(NSGA-II) and Invasive Weed Optimization (IWO). Firstly, the fast nondominated sorting algorithm was used to rank the weeds, and the number of seeds produced by a weed increased linearly from highest rank to the lowest rank. Moreover, in order to get a good distribution and spread of Pareto-front, crowding distance was used for determining the seeds numbers produced by the weeds with the same rank. Finally, the maximum number of plant population of IWO was adjusted dynamically according to the number of nondominated solutions obtained during each iteration. Then the NSIWO approach was applied to the design of a Permanent Magnet Brushless Direct Current (PMBLDC) Motor of Underwater Unmanned Vehicle (UUV). The obtained results were compared with NSGA-II which is widely used in motor optimization. Numerical results in terms of convergence and spacing performance metrics indicates that the proposed multi-objective IWO scheme is capable of producing good solutions.

The Tower of Hanoi problem is an ancient and interesting topic. In this paper, we presented an evolutionary algorithm approach for searching the solutions of the problem. We use a direct encoding and apply mutation only in the evolution. Experimental results are reported and show that the proposed method is capable of finding solutions for the problem of multiple pegs.

Multipopulational metaheuristic methods have been used to solve a variety of problems. The use of multiple populations evolved in parallel and exchanging data according to a particular communication strategy is known to mitigate premature convergence, enlarge diversity of the populations, and generally improve the results obtained by the methods maintaining a sole panmictic population of candidate solutions. Moreover, multipopulational algorithms can be easily parallelized and efficiently accelerated by contemporary multicore and distributed architectures. In this work, we study two populational real-parameter optimization metaheuristics in a traditional and multipopulational configuration, and propose a new heterogeneous multipopulational approach. The usefulness of the new method is briefly evaluated on experiments with several well known test functions for real-parameter optimization.

Economic Load Dispatch (ELD) is one of the important optimization tasks, which provides an economic condition for the power systems. In this paper, Evolved Bat Algorithm (EBA) as an evolutionary based approach is presented to solve the constraint economic load dispatched problem of thermal plants. The output generating power for all the power-generation units can be determined by the optimal technique for transmission losses, power balance and generation capacity, so that the total constraint cost function is minimized. A piecewise quadratic function is used to show the fuel cost equation of each generation unit, and the B-coefficient matrix is used to represent transmission losses. The systems with six units and fifteen units of thermal plants are used to test the demonstration of the solution quality and computation efficiency of the feasibility of the application of the Evolved Bat Algorithm for ELD. The experimental results compared with the genetic algorithm (GA) method for ELD, and with the particle swarm optimization (PSO) method for ELD, show that the applied EBA method for ELD can provide the higher efficiency and accuracy.

Ebb-Tide-Fish Algorithm (ETFA) is a simple but powerful optimization algorithm over continuous search spaces, and the inspiration comes from the foraging behavior of the fish in ebb tide. This kind of fish is a fascinating creature, and it often draws my attention when I walk on the beach. When I studied and got an idea of improving some optimization algorithms recently, the kind of fish flashes in my mind. The algorithm mainly focuses on the diversity of locations of the fish rather than what velocity it is when the fish swim from the current location to a better one. The algorithm gives a formulation of the foraging behavior of the fish, and the detailed model is also given in the paper. The performance of ETFA on a testbed of four functions is compared with several famous published methods. The final results show that ETFA has a faster convergence rate with an excellent accuracy.

Large-scale groundwater management problems pose great computational challenges for decision making because of the spatial complexity and heterogeneity. This study describes a modeling framework to solve large-scale groundwater management problems using a newly developed spatial evolutionary algorithm (SEA). This method incorporates spatial patterns of the hydrological conditions to facilitate the optimal search of spatial decision variables. The SEA employs a hierarchical tree structure to represent spatial variables in a more efficient way than the data structure used by a regular EA. Furthermore, special crossover, mutation and selection operators are designed in accordance with the tree representation. In this paper, the SEA was applied to searching for the maximum vegetation coverage associated with a distributed groundwater system in an arid region. Computational experiments demonstrate the efficiency of SEA for large-scale spatial optimization problems. The extension of this algorithm for other water resources management problems.

By adjusting the pixel-value of a host block, we design an effective steganographic method for color images. Specifically, based on a smart pixel-adjustment policy with two averages of the block, a secret message can be embedded in a host image without arising visual distortion. Experiments indicate that the perceived quality generated by the proposed method is good while the payload is larger than existing techniques. Moreover, the proposed method has a merit of maintaining a certain degree of robustness. Namely, the marked images generated by our method are tolerant of manipulations such as color quantization, equalized, edge sharpening, inversion, JPEG, JPEG2000, noise additions, pixel-truncation, winding, and zigzagging. This robustness is rarely found in the traditional techniques for color image steganography.

In this study, we propose a recognition method in ball games using no more than two triaxial accelerometers on the user’s front arm and upper arm to track motion data. To produce effective features for classifying ball games’ postures, the motion data is processed by our method, which includes a median filter, a duplication removal algorithm, and an algorithm of feature extraction. Subsequently, the produced features are recognized by a support vector machine scheme for sports with single-handed swings like tennis, badminton, and ping pong. The research result in this investigation can help the athlete training of the above mentioned sports. Experimental results showed that the precision rate of the proposed method for recognizing postures in a single-handed swing achieves 95.67%.

The paper proposes a hybrid method that combines a binary encoding method and a linear transformation scrambling technique to hide an image. Firstly, a linear transformation scrambling technique is used to rearrange the pixel values of a covert image to form a linear-transformation-scrambled matrix by using a certain linear transformation scrambling technique. Secondly, the linear-transformation-scrambled matrix is encoded into a host image to form an overt image by using a certain encoding method. The overt image contains seven groups of binary codes, i.e. identification codes, dimension codes, graylevel codes, linear slope codes, linear intersection codes, linear transformation scrambling times codes, and information codes. The parameters are used to encode and hide the covert image. According to the simulation results, the proposed method does well, larger image scrambling degree for the scrambled matrix, and saver computing time.

Hamming codes can improve the embedding efficiency by hiding messages in a block-by-block manner with pixel-flipping. But since each pixel in the block is not dispersed in the image, it can only be flipped individually thus introducing undesirable visual distortion for halftone images. Also, since the errors caused by tamper are usually more than one bit in a block, the tampered region cannot be recovered by error correction of Hamming code. This paper proposes a dispersed block generating scheme through Space-filling curve decomposition to hide data using Hamming coding into these dispersed blocks. Each block consists of pixels randomly and uniformly distributed all over the cover halftone image, and the relation between pixels in the adjacent blocks is the adjacent pixels along the Space-filling curve. Experimental results show that the proposed method significantly improves the visual quality of marked halftone images and can recover local tamper.

Medical imaging is an important part of patient records. The pixel of a 16-depth DICOM image is totally different from the 8-bit depth nature image and is seldom the same as the other pixels in the nearby area. In this paper, we propose a reversible hiding method that expands Feng and Fan’s prediction technique and adapts the scheme to match the characteristics of medical image. In the previous work, we determine what prediction method should be applied based on standard deviation thresholds to obtain more accurate prediction results. Finally, our approach includes embedding hidden information based on the histogram-shifting technique. The experimental results demonstrate that our approach achieves high-quality results.

Data compression has been at an important stage, which not only needs to achieve higher compression ration but also needs to achieve the low distortion rate. High compression can make us be able to save the same data with smaller space; it can also be save the bandwidth of data transmission on networks. The proposed method is tried to further reduce the size of digital image and improve the visual quality of the decompressed image. The experimental results shows that the proposed method has better visual quality than VQ in case of AC codebook size is greater than 1024. On the other hand, the compression rate of VQ is 0.06 and the proposed method is 0.04 when AC codebook size set as 1024.

Parking supplementary system has been such broadly used in some vehicle that we could drive and park more safely and conveniently. As light intensity has important effect on images from cameras which are mounted on vehicle’s four different directions in order to obtain panoramic view. To eliminate the influence of light to the image and obtain consistent brightness of image, this paper proposed mix-algorithm combined histogram equalization (HE) with multi-scale retina (MSR). This method makes equalization processed image to MSR, and gets a enhanced result at last. This algorithm can decrease effectively noise and enhance the brightness of image. The performance of output image is shorter time, smaller mean square error and higher peak signal to noise ratio than MSR. The experimental results demonstrate that the proposed method can not only counterpoise illumination but also meet real-time requirements. So the proposed mix-algorithm can apply in lack of brightness and higher real-time such as parking supplementary system, traffic monitoring system and security systems etc.

In this study, the effects of four indoor illuminations on e-book readers are investigated by HRV analysis. Two main types of commercial light bulbs are adopted, the saving energy and LED ones. They are all categorized as high color temperature and low color temperature. The results of HRV analysis indicate that subjects feel more awake under high color temperature illuminations, no matter the type of bulbs. Moreover, subjects perform better in Continuous Performance Test when high color temperature energy-saving bulb is used as illumination source.

Hyper-heuristics are a class of high-level search methods used to solve computationally difficult problems, which operate on a search space of low-level heuristics rather than solutions directly. Previous work has shown that selection hyper-heuristics are able to solve many combinatorial optimisation problems, including the multidimensional 0-1 knapsack problem (MKP). The traditional framework for iterative selection hyper-heuristics relies on two key components, a heuristic selection method and a move acceptance criterion. Existing work has shown that a hyper-heuristic using

Modified Choice Function

heuristic selection can be effective at solving problems in multiple problem domains.

Late Acceptance Strategy

is a hill climbing metaheuristic strategy often used as a move acceptance criteria in selection hyper-heuristics. This work compares a

Modified Choice Function

-

Late Acceptance Strategy

hyper-heuristic to an existing selection hyper-heuristic method from the literature which has previously performed well on standard MKP benchmarks.

The computational modeling of neurobiological phenomena is an important area in bio-inspired computing. The understanding of state of consciousness as cognitive function is central to it. The functioning of neurological structures is inherently distributed in nature having a closer match to distributed computing. However, the role of functional neurophysiology is critical in cognition modeling. This paper proposes a mathematical model of state of consciousness by mapping the functional neurophysiology and by inducing distributed computing structures in coherence. The scopes of evolution of consciousness and memory are incorporated into the model. The numerical simulation of the distributed computational model is conducted by considering different choice functions. The results illustrate that, gradual evolution of positive consciousness is deterministic under fair excitation from environment.

The rotation invariant texture classification is an important application of texture analysis. A rotated texture is often perceived by the changed dominant direction. This paper proposes an effective rotation-invariant texture classification method by combining the local patch based method with the orientation estimation. For a texture sample, the Principal component analysis is applied to its local patch to estimate the local orientation, and then the dominant orientation is determined with the maximum value of the local orientation distribution. In order to extract the feature vector, each local patch is rotated along the dominant orientation after circular interpolation. By using the random projection, the local gray value vector of a patch is mapped into a low-dimensional feature vector that is placed in the bag of words model, together with local orientation feature. The simulation experiments demonstrate the proposed method has a comparable performance with the existing methods.

Because uniform division spatial histogram can not finely divide the data in relatively concentrated areas, it can not accurately track human faces. A new face tracking method which combines an improved spatial histogram with particle filter is proposed. In this method, non-uniform division is proposed. Histogram data in relatively concentrated areas can be divided finely, and histogram data in relatively sparse areas can be divided roughly. Simultaneously, a new re-sampling method is proposed in order to solve the "particle degradation" and "particle depletion". If many duplicate particles occur, keep a particle, remove other particles. In order to ensure that the total number of particles is N, particles must be selected randomly in the vicinity of the particles which have a large weight. Experiments show that its tracking performance is very good when target color is similar to the scene color and obstructed partly or completely, or under the complex non-linear, non-Gaussian situations.

Due to low resolutions of infrared face image, the local feature extraction is more appreciated for infrared face feature extraction. In the current LBP (local binary pattern) feature extraction on infrared face recognition, single scale is encoded, which consider limited local discriminative information. A new infrared face recognition method based on joint encoding of multi-scale LBP (JEMLBP) is proposed in this paper. To consider correlation in different micro-structures, co-occurrence matrix of multi-scale LBP codes is used to represent the infrared face. The experimental results show the recognition rates of infrared face recognition method based on JEMLBP can reach 91.2% under variable ambient temperatures, outperforms that of the classic method based on single scale LBP histogram.

With the increase in the number of private cars as well as the non-professional drivers, the current traffic environment is in urgent need of driving assist equipment to timely reminder and to rectify the incorrect driving behavior. In order to meet this requirement, this paper proposes an innovative algorithm of driving behavior analysis based on AdaBoost with a variety of driving operation and traffic information. The proposed driving behavior analysis algorithm will mainly monitor driver’s driving operation behavior, including steering wheel angle, brake force, and throttle position. To increase the accuracy of driving behavior analysis, the proposed algorithm also takes road conditions into account. The proposed will make use of AdaBoost to create a driving behavior classification model in various different road conditions, and then could determine whether the current driving behavior belongs to safe driving. Experimental results show the correctness of the proposed driving behavior analysis algorithm can achieve average 80% accuracy in various driving simulations. The proposed algorithm has the potential of applying to real-world driver assistance system.

In this paper, we are going to introduce PRINCE2 ,which is the advanced international projects management methodology, to build a model of projects management and control based on PRINCE2 & CMMI for enterprise group that has already been implementing CMMI in order both to manage and control the feasibility analysis, business justification, process monitor, resources management, appraisal of IT projects and ensure IT projects are necessary, feasible, economical, controllable, investment effective as well.

Now-a-day, it is necessary to modify image size for fitting different display devices. This process is called image retargeting. If users hide a secret image into a host image. The hiding data may loss after the retargeting process. This paper proposed a data hiding method which the secret image will not loss after the image has been retargeted. Based on the fault-tolerance property of secret image sharing scheme, many shares are generated from a secret image. The shares are embedded in many locations of the host image. This means the hiding data may not delete after the image has been retargeted. The result is suitable for the video copyright protection between different devices. An experiment is also presented.

A method of filling 3D model using generated solid textures is proposed. Using 3D texture synthesis, a 2D exemplar texture is synthesized to be a solid texture. The synthesized solid texture can be the stuffed material of 3D Solid model or the solid material of surficial mesh. The proposed method designs two algorithms to cutting the model. After cutting, we will repair the cross section. It makes the surface model look like the solid model. And it is real time display when cutting. Because, we can preview the 3D model and it internal cross section before printing, the application can also be combined with 3D printing.

Carpal tunnel syndrome (CTS) is commonly occurred in occupations using vibrating manual tools or handling tasks with highly repetitive and forceful manual exertion. Recently, the ultrasonography has been used to evaluate CTS by monitoring median nerve movements. In order to facilitate the automatic extraction of shape characteristics for the median nerve, this paper designed a procedure that used greedy active contour detection model (GACD) to detect the edge of median nerve in ultrasound image. We selected a ROI to be an initial of virtual contour for median nerve in original ultrasound image. That can enhance the sensitivity of proposed GACD model to detect the contour of median nerve. In the experiment, the results show that the performance of the method is feasible and accurate.

A method of decomposition into sub-concept lattices with the same attributes set is proposed. Based on the decomposition function, it is proved that when all the nodes of the concept lattice are arranged in ascending order of the extent base, the node of the sub-concept lattice is generated and its intent is equal to the intent of the node decomposed if the extent of the first decomposition; at the same time, the Hasse diagram of the sub-lattices is generated according to the route number of the node generated, then the method is described. Finally, the effectiveness of the developed method is illustrated by an example.

Lack of strategic information planning and implementation assessment methods, especially the effect and the completion rate for enterprise information planning, this paper puts forward an evaluation method called EIP (Planning and Implementation Evaluation) model based on PDCA Cycle, it carries out in planning, doing, checking and action. This paper also proposes a Comprehensive Evaluation Indicators (CEI) formula which can compute the completion rate in line with the information planning target. As preliminary results show the evaluation method can enhance the accuracy of the implementation planning as scheduled, and also play an important role in guiding the enterprise information implementation.

This paper proposes a new way of looking into Web page ranking systems by using some concepts of queuing theory in operations research and stochastic water storage theory in hydrology. Since both theories queuing and stochastic water storage are rich in technology as well as application aspects, the new look in this paper may lead to new directions in Web page ranking systems and related research areas. In doing so, first this paper draws some analogies between a Web page ranking system and theory of queues. Then it shows how a Web page ranking system can be tackled to reduce current obstacles by using queuing theory techniques. In the second, a Web page ranking system is modeled as a framework of stochastic water storage theory to derive a list of Web page rankings. Third and finally, the outcome results of rankings obtained by using the proposed two theories queuing theory and stochastic water storage are compared and analyzed analytically as well as experimentally. The experimental results show the proposed new look is promising for establishing a new research area which can improve the current situations and difficulties occurred in search engines and their ranking systems in particular and some problems in World Wide Web as a whole.

To guarantee the effective communications under earthquake and enhance the utility of communication system in nuclear power plants, it is proposed to do seismic qualification test for all telecommunication system in nuclear power plant. In this paper, a principle include object, scope method and setup of the qualification is provided. It is found that if telecommunication equipments pass such qualification test, the availability of communication system and the safe operation of nuclear power plant can be guaranteed when earthquake occur.

The cloud computing is one of the most popular issues in recent years. Many service providers have provided the cloud solution using virtualization such as Amazon EC2. We are facing the new threats in the virtual environment. Since the virtual network is different to the traditional environment, we have to face with new threats that do not exist in the traditional network environment. In this paper, we provide a solution Virtual Network Guard System (VNGS) to solve the problems in virtual network that we face in the virtual environment. We modify the network interface controller in the virtual environment to limit the guest operation system access rights. We also provide a centralize management server to deploy filtering processes and collect the alert information. Finally, we evaluate the performance of our system with normal network interface controllers, and the results shows that the performance is acceptable.

With the development of network technology, database-driven web applications (apps) provide flexible, convenient, available, and various services for users. User can send requests to these web apps by using browser over the Internet to get services such as e-commerce services, entertainments, and financial services. Though web environments have several advantages, various security threats have been described. Among these threats, SQL injection attack (SQLIA) is one of the most serious threats. SQLIA is a code injection attack that exploits secure vulnerabilities consisting in source codes to attack databases. SQLIA allows attackers to bypass authentication, access private information, modify data, and even destroy databases. Since many sensitive and confidential data stored in database must be kept private and secure, a mechanism to detect SQLIAs for web environments is necessary. In this paper, we define a framework named DSD (Dynamic SQLIAs Detection) to counter SQLIAs in web environments. Then, a concrete detection mechanism based on DSD is proposed to detect SQLIAs by using parse tree. The experimental results are demonstrated that our mechanism has higher accuracy, lower false positive rate, and false negative rate.

With the development of computer vision, there has been an increasing need to develop objective quality measurement techniques that can predict image quality automatically. In this paper, we present a complex No-reference image quality assessment (NR IQA) algorithm, which mainly consists of two steps. The first step uses Gabor filters to obtain the feature images with different frequencies and orientations, so as to extract the energy and entropy features of each sub-image. The second step uses the Linear least squares to obtain the parameters for IQA. We conduct experiments in LIVE IQA Database to verify our method. The experimental results show that the proposed method is much more competitive than other state of the art Full-reference (FR) or NR algorithms.

Nowadays chipped based sensors and RFID tags are widely employed in Internet of Things; however, for those devices, effective and flexible security mechanisms lack. In this paper we study the security requirement and propose an adaptive security framework for sensors in Internet of things, which provides dynamic confidentiality, authenticity and integrity in the networks with relative suitable overhead by context aware computing, decision making and dynamic enforcement of policies. We employ Markov Decision Process to make the decisions of security actions and adopt aspect-oriented programming technique to enforce the security policies dynamically in the working networks. We made simulations of our framework, and the performance is encouraging.

Current recommender systems are mainly based on customers’ personal information and online behavior. We find that those systems lack efficiency and accuracy. At the same time, we observe the large amount of review data with exponential growth. Based on this observation, we propose a recommender system based on opinion mining. With text mining method we extract the opinion related information from the massive reviews. We analyse the linguistic information and design a two-layer selection algorithm to find the most suitable products for customers. The experiment shows our method has great accuracy, fleasibility, and reliablity.

The modern computer and network system, is composed of tens of thousands of components. Any faults in components can affect reliability of the whole system. In the process of running, elements will gradually age and failure rate gradually increase. However, the rate at which these elements age is not synchronous. Thus, in which cases should the elements be upgraded or replaced and in which cases should the elements whose performance is still relatively good be retained, are hard job to handle. In this paper, the author attempts to make an extreme analysis on the reliability of parallel system with N element obeying exponential distribution to improve the system update.

In the process of rapid development of large enterprises, some independent and closed business requirements arise,such as the core design, production data, security monitoring and so on.These businesses are required for carrying on the private network seprarated from the enterprise office network, but it can not be completely isolated because it exists business integration needs and economic demands such as saving investment, simple operation, so it brings a contradiction between the private network construction and cost control. In this paper, combining with the firewall technology and the optimization network framework, we give the basic ideas and implementation solutions to this contradiction.