One of methods of improvement of the material flow is cross docking that In is considered as a good method to reduce inventory and improve customer satisfaction. Too, the vehicle routing problem is one of the important problems in distribution management and its goal is to find paths for delivering various cargos. Therefore, this study was conducted in order to vehicle routing problem with Cross-docking in MEHRIZ SHADI MEHREGAN Biscuit and the problem was solved with a Scatter search Algorithm. The findings concluded that the optimal distance by Scatter search Algorithm was equal to 11,438 km that Compared with the current status that improved respectively of 30.54%. The optimal cost by Scatter search Algorithm was equal to 10186655 that Compared with the current status and improved 6.7%. Therefore, can be concluded that the Scatter search Algorithm is a good solution to this problem.

Because cyberspace and Internet predominate in the life of users, in addition to business opportunities and time reductions, threats like information theft, penetration into systems, etc. are included in the field of hardware and software. Security is the top priority to prevent a cyberattack that users should initially be detecting the type of attacks because virtual environments are not monitored. Today, email is the foundation of many internet attacks that have happened. The Hackers and penetrators are using email spam as a way to penetrate into computer systems junk. Email can contain viruses, malware, and malicious code. Therefore, the type of email should be detected by security tools and avoid opening suspicious emails. In this paper, a new model has been proposed based on the hybrid of Scatter Searching Algorithm (SSA) and K-Nearest Neighbors (KNN) to email spam detection. The Results of proposed model on Spambase dataset shows which our model has more accuracy with Feature Selection (FS) and in the best case, its percentage of accuracy is equal to 94. 54% with 500 iterations and 57 features. Also, the comparison shows that the proposed model has better accuracy compared to the evolutionary Algorithm (data mining and decision detection such as C4. 5).

This paper presents a multi-objective cell formation problem considering alternative process routes and machine utilization. Three conflicting objectives, namely 1) minimizing the total cost consisting inter-cell movements, procurement, operation and maintenance; 2) maximizing the total machine utilization in the manufacturing system; 3) minimizing the deviation levels between the cell utilization (i.e., balancing the workload between cells) are considered. Since this problem is NP-hardness, a multi-objective Scatter search (MOSS) method based on TOPSIS is designed in order to find locally Paretooptimal frontier. To demonstrate the efficiency of the proposed MOSS, a number of test problems are solved and the related results are compared with the well-known multi-objective genetic Algorithm, called NSGA-II, based on some quality measures. The computational results indicate the superiority of the proposed MOSS compared to the foregoing NSGA-II.

Parameter estimation is an important task in the modeling, classification, and detection of radar clutters. Radar clutters have stochastic characteristics. Therefore, Statistical distributions are usually used to describe the features of clutters better. K distribution is one of the most common models utilized to the simulation of clutters. This distribution, which consists of scale and shape parameters, has two speckle and local power components. Because local power component is modeled by gamma distribution, the parameter estimation of K distribution is a high dimensional and nonlinear problem. In this paper, a novel method is proposed based on the gravity Searching Algorithm for the parameter estimation. This new method has high accuracy and validity in estimating parameters. For the evaluation of the proposed method, the estimated probability density function and power spectrum in two different experiments were compared to actual ones. Finally, the results of the new method are compared to the results of the maximum likelihood method. Furthermore, K-S test is performed to evaluate generated clutters with estimated parameters. Results prove the validity of the proposed method for the parameter estimation.

We present an Algorithm for NVIDIA CUDA platform based on Smith-Waterman Algorithm for sequence alignment problem. CUDA is a new programming language which is very similar to the standard C language, with some extensions. By using the Smith-Waterman Algorithm which is used to find similarity between two sequences by making a scoring matrix; this application tries to find the similarity between a sequence which called query sequence and sequences in a database file. In this program, each thread is used for calculating one scoring matrix between the query sequence and one of the sequences in database. The Algorithm utilizes small but fast shared memory which is inside the GPU for holding four intermediate columns in each cycle for each thread. If the database is large enough, it can keep the GPU in full working order and results in better speedup in comparison to CPU. To demonstrate the performance, we used a high-end CPU, Intel Core 2 Due E6600 and a mid-end GPU, GeForce 8600GT and saw the speedup in about twenty times more than CPU.

In the last few years, the computer network technology has been improved by a large scale and therefore has influenced the application of distributed data base systems for a better reliability, performance and inexpensive query processing. Hence, new application and data base architecture for distributed data base system need to be developed and several challenging issues have to resolved by research communities around the world. The major challenging issues for this new generation of data base system deal with allocation, replication and portioning data and influences of each other for a better design.In this paper, we present a heuristic and efficient Algorithm to solve the problem of data portioning and allocation at the same time. The data portioning is vertically broken into two or more segments that are most efficiently allocated to data base sites. To provide an efficient and cost effective portioning and allocation, an object function is developed and used for each iteration to intelligently getting closer to the optimized result. To examine the correctness of the Algorithm, a typical problem is solved and then the results are discussed in this paper. A software package is implemented to be used for designing a distributed data base system

The vehicle routing problem (VRP) is an important scientific problem addressed in distribution management. In classical VRP, the vehicle load is often regarded as a constant during transportation; therefore, the loading cost associated with the amount of the load on the vehicle, are neglected in the objective function when optimizing a vehicle routine. However, in real-world, the vehicle load varies from one customer to another in a vehicle route. Thus, the vehicle route without considering the effect of loading cost may lead to sub-optimal routes. In this paper, we investigate VRP with loading, which considers the costs associated with the amount of the load on the vehicle when determining the vehicle routes. This paper presents a new Pareto-based multi-objective approach that uses a Multi-objective Scatter search strategy for solving a multi-objective formulation of the VRP that aims to minimize the total distance of the vehicles used to service the customers, while also minimizing the imbalance of workloads (distances travelled/goods delivered by the vehicles). This new approach is evaluated in comparison with two well-known multi- objective Algorithms, MOPSO and MODE. The results obtained when solving a subset of benchmark problems show the good performance of this approach. The results demonstrate that the suggested method is highly competitive.

Peer to peer network is a set of self-adaptive and identic nodes that cooperate together without the central coordinator. Mobile peer to peer network is caused from the implementation of peer to peer network over mobile Ad-hoc networks’ platform. The distributed structure of these networks reinforces the resource Searching. The requests in these networks are often propagated randomly and broadcast. Thus, designing a self- adavptive mechanism that sends the request intentionally to the network is crucial, to be able to discover the demanded source of the request with the maximum amount of success rate and minimum amount of energy and traffic load. In this research is presented solution with the aim of improving serach on the mobile peer to peer network. Learning automata are adaptive decision making units which run in random environments that learn the optimal action through repeated interaction with its environment. In the proposed Algorithm, each node having a message sends a search request to some more useful neighbors. Correspondingly the best route from the source node will transmit the requested resource. Moreover Because of saving result in neighbourse cache table, Neigh bourse experince are effective in future forwards. The simulation results of the proposed Algorithm and comparison with the “Gossiping Load Balancing alghorithm” Algorithm and “Imporoved Adaptive Probablitistic Search”, show that avoiding selecting random neighbors for forward request and considering the history of each node using the results stored in cache tables, reduce energy consumption, the number of sended and droped network packets, and the network load. Since, due to the influence of neighboring nodes in proceeding, the request message is sent to the neighbors who have been more successful in previous searches, the success rate of the network also increases.