# Genetic Algorithm Based Interconnection Network Topology Optimization Analysis

Save this PDF as:

Size: px
Start display at page:

Download "Genetic Algorithm Based Interconnection Network Topology Optimization Analysis"

## Transcription

1 Genetic Algorithm Based Interconnection Network Topology Optimization Analysis 1 WANG Peng, 2 Wang XueFei, 3 Wu YaMing 1,3 College of Information Engineering, Suihua University, Suihua Heilongjiang, , School of Electrical Engineering, Suihua University, Suihua Heilongjiang, Abstract As the size of distributed systems becomes larger and larger, route planning is an important issue in such systems. However, an optimal topology of such systems could make route planning more efficient, and improve the performance of these systems a lot. In this paper, we aim to optimize the topology of interconnection networks in these distributed systems to make route planning more efficient. We formalize this optimization problem into an integer programming problem, and proposed a genetic algorithm based topology optimization algorithm. Experiments on real and artificial datasets show that our proposed algorithm is not only more efficient than traditional artificial intelligence algorithms, but also has the smallest difference with respect to the optimal solution. 1. Introduction Keywords: Genetic Algorithm, Interconnection Network, Topology One of the most important components of a computer system is its interconnection network, as it defines the physical layout of such systems. An interconnection network may consist of nodes which represent computers, switches, terminals, or printers, and edges which denote communication links. These links can be conventional telephone lines, optical fiber cables or microwave channels. In such systems, each node communicates with others across the links. As the number of the nodes in such systems becomes bigger and bigger, which make communication in such systems a heavy problem, route planning is a hot research in computer networks. Therefore, the suitability of such a network for various scientific and engineering applications can be assessed by careful analysis of its performance in terms of its route planning. So, there is always a challenge to design a highly reliable system, which is quite economical. An important stage of the design of such systems is to find the best layout of components to minimize cost while meeting a performance criterion, such as the route planning. In interconnection networks [1], an optimal topology is a structure that both satisfies the route planning and minimizes the cost of the system from the perspective of route planning. In topology optimization, one usually starts with a ground structure. The member set of an optimal structure is a subset of members of the selected ground structure. Hence, topology optimization is the optimal problem of finding a subset from a complex interconnection network. In this paper, we formalize this problem into finding a minimal spanning tree from a graph. A general definition of the topological design problem for a large hierarchical network is found in [2]. Genetic algorithm (GA) is a search strategy based on the rules of genetic evolution [3, 4]. GA has been successfully applied to optimization problems in various fields such as computer science, social science, operations research and so on. The examples of application are summarized in Ref. [5]. Recently, some attempts have been made to apply GA to predict the water quality [6], analyze the blasting effect [7], solve the traditional Traveling Salesman Problem [8], and evaluate the sludge compost quality [9]. In GA, the gradients of the cost function and the constraint functions are not needed to find approximate optimal solutions. Therefore, GA is quite useful for a problem of which the cost function is a discontinuous function of the design variables. Grieson and Pak [10]applied GA to find optimal topologies of frames. In their method, however, stress constraint is not considered. Moreover, Liu et al. [11] apply GA to solve combinatorial optimization problem using improved GA. At present, GA is using extensively in the field of topology optimization. Contrast to traditional methods, there are some advantages using genetic algorithms to solve the problem of network topology optimization: it can get optimal or sub-optimal solutions effectively better than the traditional way in Journal of Convergence Information Technology(JCIT) Volume8, Number9, May 2013 doi: /jcit.vol8.issue

2 larger search area; the genetic and variation that it simulates make the problem more controllable and uncontrollable and has bigger contraction and operating space. In this paper, we study the problem of topology optimization in interconnection networks. First, we formalize this optimization into an integer programming problem. Then, we design a genetic algorithm to solve this integer programming problem. Finally, we compare our proposed algorithm with traditional mountain climbing algorithm and simulated annealing algorithms. Experiments on real and artificial datasets show that our genetic algorithm based topology optimization algorithm not only has shortest execution time, but also has smallest difference with respect to the optimal solution. The rest of the paper is organized as follows: in section 2, we review related work on topology optimization by the genetic algorithm; in section 3, we formalize the optimization problem into an integer programming problem, and propose a genetic algorithm based topology optimization algorithm; we do some experiments to validate the efficiency of the proposed algorithm in section 4; and conclusion is given in section Related work In this section, we review related work on topology optimization problem using the GA. Based on quality of service of network planning, Xu et al. [12]studied the initial network that satisfies the connectivity and hop count constraints using hybrid genetic algorithm, and analyzed the runtime operation under network failure. In order to deal with the link flow changes caused by the failure, they proposed a heuristic algorithm, and improved the local network topology using the algorithm. When a failure occurs in the network, they ensure the normal operation of the network by adjusting the link to meet the minimum cost of network. As the paper just adjusts the network locally in the case of network failures, does not improve or optimize the network under normal situations, and thus has limited usefulness of the entire network topology optimization. Chen et al. [13] proposed a new topology optimization method, called GAOC, based on a hybrid GA. The GAOC method uses evolutionary mechanism of GA and interpolation method of optimization criterion. The interpolation method initiates the input of GA, and then searching the specific region by the GA. The combination of the interpolation method and the evolutionary mechanism can minimum the weight of the topology better. Their experiments show that the GAOC method is efficient in solving general topology optimization problem, that is obtain higher computation result with lower cost. Based on distributed nodes matrix encoding method, Su et al. [14] analyzed and optimized the topology of truss by adaptive GA. They designed crossover and mutation factor on the matrix chromosomes, and the adaptive policy effectively improved the robustness of genetic algorithm. As they filtered out invalid factors such as kinematics and structural instability before the evaluation, their method saved some computation time. Proved through two example of truss delay, the algorithm had high convergence and robustness. The limitation of that algorithm is that it is designed for truss topology only. Teng and Zhou [15] introduced the Pareto optimum theory, the famous Pareto ranking technology and genetic algorithm based on Pareto ranking theory, and proposed a dual designing rules algorithm, called PCGA. The PCGA algorithm mainly contained two aspects, the average delay and the network cost. At the same time, they used the Prufer number to set the number of chromosomes to ensure the structure of the network topology in the LAN network. Moreover, they revised the traditional crossover method, and accelerated the mutation method. Comparing with classical searching method, this algorithm is correct and efficient, but it is hard to implement, and has nothing to do with the improvement of the quality of service. After analyzing the intrinsic properties of Network coding, Kun et al. [16] studied how to get maximum multicast rate and minimum resources in linear coding. As that problem is NP-complete, they applied the improved genetic algorithm in algebra framework. Combined with the authentication mechanism of random polynomials, their algorithm degraded the network coding in network topology. They added new variable at the beginning of the loop of the genetic algorithm. Hence, in order to avoid the long optimization time of traditional genetic algorithms, they introduced a method of binary variation. The experiments showed that the improved genetic algorithm had fast convergence speed and optimization speed, and thus it can be applied to the optimization of network coding. Glass et al. [17] analyzed and designed the embedded system in a network, and one main challenge is the design of optimal route in such systems. They proposed an improved genetic algorithm that can 357

3 be applied to multi objective evolutionary algorithms. Their algorithm could make sure the optimization operation of the route network topology. They also did some experiments to validate the efficiency of their algorithm. Jonathan et al. [18] designed an electromagnetic solenoid by the network topology optimization method. They proposed the original network topology optimization tool based on simulated annealing algorithm. Contrast to other network topology optimization tools, this new tool used the genetic algorithm, and it could be used in the application of switched reluctance motor. 3. Genetic algorithm based topology optimization algorithm 3.1. Formulation of Optimization Problem An interconnection network can be viewed as an equivalent graph G (N, E, W) where, N represents the nodes (stations, terminals or computer sites), E is the communication links among them, and W is the weight of its communication link. If the nodes of the network are fixed, then the main design decision in route planning is to maximize the weight of its minimal spanning trees. The integer programming formula is as follows: Objective function: n 1 n Max f ( x)= wij xij (1) Subject to: n 1 n i 1 j n 1 i 1 j n 1 xij n 1 (2) n 1 n xij S 1, S V \{1}, S 2 (3) i S j S j 1 Where formula (1) is the objective function, whose purpose is to find a minimum spanning tree with the maximum weight, formula (2) means that a tree has n-1 edges, and formula (3) represents that there is no circle Genetic algorithm and its implementation Coding and decoding In the coding and decoding step, we use the Prufer number to transform from a tree to a Prufer, more details about the Prufer number can be seen in [15]. Examples of transformation between tree and Profer number is in figure 1 and 2. Figure 1. Coding ( tree Prufer number ). 358

4 3.2.2 Initial Population Figure 2. Decoding ( Prufer number tree ). The initial population is generated in 3 phases. (1) Find two minimal spanning trees T 1 and T 2 of G on the basis of cost to Reliability ratio and (1-R). (2) Generate all paths p i, i=1, 2, from s to t of G. (3) Merge each p i with T 1 and T 2 separately till the cost constraint of the network is not satisfied Selection operator In this paper, we use a certain selection operation ( ), that is choose the best chromosomes from both the parent and the child. This will make sure that optimal chromosomes can be retained in the new generation Crossover operator We use uniform crossover as our crossover operator. The uniform crossover is that each gene of the two individual crossover with the same probability, and thus forming two new individual. The details are: generating a binary masking word randomly with the same length as encoding, W=w 1 w 2 w l, l is the length of individual coding; generating two new individuals A and B from parent A and B according the following rules: if w i =0, the ith gene of A comes from A, and the ith gene of B comes from B; if w i =1, the ith genes of A and B change with each other, and thus we get A and B. The Crossover process can be described in figure Mutation operator Figure 3. Example of uniform crossover. We apply the random perturbation mutation as our Mutation operator. The random perturbation mutation is that selecting a gene randomly, generating an integer between 1 and n (number of nodes), and replacing the gene with the generated integer. 359

5 3.2.6 Genetic algorithm Genetic algorithm based topology optimization algorithm Input: a graph G (N, E, W); Output: a tree G (N, E, W ); 1: coding; 2: let t 0; 3: initiate P(t); // Initial Population 4: evaluate P(t); 5: while not termination do 6: crossover P(t); 7: Mutation P(t); 8: let C(t) be the child generated by P(t); 9: evaluate C(t); 10: choose P(t+1) from P(t) and C(t); 11: t t+1; 12: end while 13: decoding 14: return G ; 4. Experiments In this section, we do some simulated experiments to test the efficiency of our algorithm. In order to demonstrate the robustness of our algorithm and to show its performance, we do the experiments on two interconnection datasets, one is generated artificially, and the other one is our real experiment platform. The details of datasets are in table Results Table 1. Details of datasets dataset Real network Artificial network #node #edge We compare our genetic algorithm based topology optimization algorithm with the mountain climbing and the simulated annealing algorithm to validate its efficiency. First, we compare their execution time when they all find a solution. The result is in figure 4. From the figure we can see that the genetic algorithm has the smallest execution time, whereas the mountain climbing algorithm and the simulated annealing algorithm performance better in different datasets. 360

6 time (minute) Genetic algorithm Mountain climbing Simulated annealing 20 Figure 4. Comparison of execution time. The optimal problem in formula (1) is NP-complete, and the solutions in all these three algorithms are all approximate solutions. We compare all these three approximate solutions with the optimal solutions, and analyze their differences with the optimal solution. Details are in figure 5. In our metrics, we define the difference as follows: approximate solution optimal solution diff = 100% (4) optimal solution 30 Genetic algorithm difference (100%) Mountain climbing Simulated annealing Figure 5. Comparison of execution time. From figure 5 we can see that our genetic algorithm based topology optimization algorithm has the smallest difference compared with the mountain climbing algorithm and the simulated annealing algorithm. In general, our genetic algorithm based topology optimization algorithm not only has shortest execution time, but also has smallest difference with respect to the optimal solution. 361

7 5. Conclusion The performance of interconnection network is mainly decided by its topology, and better topology will bring better route planning, and thus bring better performance improvement of the distributed systems. First, we formalize this optimization into an integer programming problem. Then, we design a genetic algorithm to solve this integer programming problem. Finally, we compare our proposed algorithm with traditional mountain climbing algorithm and simulated annealing algorithms. Experiments on real and artificial datasets show that our genetic algorithm based topology optimization algorithm not only has shortest execution time, but also has smallest difference with respect to the optimal solution. 6. Acknowledgements The research of integrated scheduling problem Based on double resources constraint. Project Number: The research and analysis of workshop production integrated scheduling algorithm. Project Number:SJ References [1] R. Rastogi, Nitin and D.S. Chauhan, Fast Interconnections: A Case Tool for Developing Faulttolerant Multi-stage Interconnection Networks, IJACT: International Journal of Advancements in Computing Technology, Vol.2, No.5, pp.13-24, [2] R. Boorstyn and H. Frank, Large-scale network topological optimization, Communications, IEEE Transactions on, Vol.25, No.1, pp.29-47, [3] J.H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press, [4] D.E. Goldberg and J.H. Holland, Genetic algorithms and machine learning, Machine Learning, Vol.3, No.2, pp.95-99, [5] D.E. Goldberg, Genetic algorithms in search, optimization, and machine learning, [6] C. Hsu, L. Chen, T. Wang and Y. Hsu, Applying Genetic Algorithm Operation Tree for Predicting The Total Phosphorus in A Reservoir, JCIT: Journal of Convergence Information Technology, Vol.8, No.1, pp , [7] Z. YANG, C. WANG and L. GUO, Optimization for the Row Heights in Medium-Length Hole Blasting Design by Genetic Algorithms, AISS: Advances in Information Sciences and Service Sciences, Vol.5, No.1, pp.5-15, [8] Y. Yu and L. Hui, Improved Quantum Crossover Based Genetic Algorithm For Solving Traveling Salesman Problem, IJACT: International Journal of Advancements in Computing Technology, Vol.5, No.1, pp , [9] J. Tian, M. Gao and C. Kong, Sludge Compost Quality Evaluation Based on Improved Genetic Algorithm and Radial Basis Function Neural Network, JCIT: Journal of Convergence Information Technology, Vol.8, No.1, pp , [10] D.E. Grierson and W.H. Pak, Optimal sizing, geometrical and topological design using a genetic algorithm, Structural And Multidisciplinary Optimization, Vol.6, No.3, pp , [11] H. Liu, Q. Wu and X. Yan, Solve Combinatorial Optimization Problem Using Improved Genetic Algorithm, AISS: Advances in Information Sciences and Service Sciences, Vol.5, No.1, pp.1-8, [12] H. Xu, L. Sun and Z. Xu, Network Plan Based on Quality of Service, Computer Engineering, Vol.3, pp.36, [13] Z. Chen, L. Gao, H. Qiu and X. Shao, A GAOC method for topology optimization design, Intelligent Computation Technology and Automation, ICICTA'09. Second International Conference on, pp , [14] R. Su, L. Gui and Z. Fan, Topology and sizing optimization of truss structures using adaptive genetic algorithm with node matrix encoding, Natural Computation, ICNC'09. Fifth International Conference on, pp ,

8 [15] F. Teng and G. Zhou, The research of an approach to design local area network topology based on genetic algorithm, Computational Intelligence and Design, ISCID'09. Second International Symposium on, pp , [16] H. Kun, Z. Jin and B. Wang, Improved Genetic Algorithm Applied to Optimization of Linear Network Coding, Wireless Communications Networking and Mobile Computing (WiCOM), th International Conference on, pp.1-4, [17] M. Gla ss, M. Lukasiewycz, R. Wanka, C. Haubelt and J. Teich, Multi-objective routing and topology optimization in networked embedded systems, Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS International Conference on, pp.74-81, [18] D. Jonathan, D. Bruno and B.A. Hamid, Simulated annealing and genetic algorithms in topology optimization tools: a comparison through the design of a switched reluctance machine, Power Electronics Electrical Drives Automation and Motion (SPEEDAM), 2010 International Symposium on, pp ,

### Numerical Research on Distributed Genetic Algorithm with Redundant

Numerical Research on Distributed Genetic Algorithm with Redundant Binary Number 1 Sayori Seto, 2 Akinori Kanasugi 1,2 Graduate School of Engineering, Tokyo Denki University, Japan 10kme41@ms.dendai.ac.jp,

More information

### MULTI-PHASE FUZZY CONTROL OF SINGLE INTERSECTION IN TRAFFIC SYSTEM BASED ON GENETIC ALGORITHM. Received February 2011; revised June 2011

International Journal of Innovative Computing, Information and Control ICIC International c 2012 ISSN 1349-4198 Volume 8, Number 5(A), May 2012 pp. 3387 3397 MULTI-PHASE FUZZY CONTROL OF SINGLE INTERSECTION

More information

### Sensors & Transducers 2015 by IFSA Publishing, S. L. http://www.sensorsportal.com

Sensors & Transducers 2015 by IFSA Publishing, S. L. http://www.sensorsportal.com A Dynamic Deployment Policy of Slave Controllers for Software Defined Network Yongqiang Yang and Gang Xu College of Computer

More information

### A Service Revenue-oriented Task Scheduling Model of Cloud Computing

Journal of Information & Computational Science 10:10 (2013) 3153 3161 July 1, 2013 Available at http://www.joics.com A Service Revenue-oriented Task Scheduling Model of Cloud Computing Jianguang Deng a,b,,

More information

### Multiobjective Multicast Routing Algorithm

Multiobjective Multicast Routing Algorithm Jorge Crichigno, Benjamín Barán P. O. Box 9 - National University of Asunción Asunción Paraguay. Tel/Fax: (+9-) 89 {jcrichigno, bbaran}@cnc.una.py http://www.una.py

More information

### A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem

A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem Sayedmohammadreza Vaghefinezhad 1, Kuan Yew Wong 2 1 Department of Manufacturing & Industrial Engineering, Faculty of Mechanical

More information

### A Binary Model on the Basis of Imperialist Competitive Algorithm in Order to Solve the Problem of Knapsack 1-0

212 International Conference on System Engineering and Modeling (ICSEM 212) IPCSIT vol. 34 (212) (212) IACSIT Press, Singapore A Binary Model on the Basis of Imperialist Competitive Algorithm in Order

More information

### A Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number

A Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number 1 Tomohiro KAMIMURA, 2 Akinori KANASUGI 1 Department of Electronics, Tokyo Denki University, 07ee055@ms.dendai.ac.jp

More information

### Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm

Journal of Al-Nahrain University Vol.15 (2), June, 2012, pp.161-168 Science Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm Manal F. Younis Computer Department, College

More information

### Keywords: Travelling Salesman Problem, Map Reduce, Genetic Algorithm. I. INTRODUCTION

ISSN: 2321-7782 (Online) Impact Factor: 6.047 Volume 4, Issue 6, June 2016 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study

More information

### Comparison of Optimization Techniques in Large Scale Transportation Problems

Journal of Undergraduate Research at Minnesota State University, Mankato Volume 4 Article 10 2004 Comparison of Optimization Techniques in Large Scale Transportation Problems Tapojit Kumar Minnesota State

More information

### 14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO)

Overview Kyrre Glette kyrrehg@ifi INF3490 Swarm Intelligence Particle Swarm Optimization Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) 3 Swarms in nature Fish, birds,

More information

### Research on a Heuristic GA-Based Decision Support System for Rice in Heilongjiang Province

Research on a Heuristic GA-Based Decision Support System for Rice in Heilongjiang Province Ran Cao 1,1, Yushu Yang 1, Wei Guo 1, 1 Engineering college of Northeast Agricultural University, Haerbin, China

More information

### ISSN: 2319-5967 ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 3, May 2013

Transistor Level Fault Finding in VLSI Circuits using Genetic Algorithm Lalit A. Patel, Sarman K. Hadia CSPIT, CHARUSAT, Changa., CSPIT, CHARUSAT, Changa Abstract This paper presents, genetic based algorithm

More information

### Wireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm

, pp. 99-108 http://dx.doi.org/10.1457/ijfgcn.015.8.1.11 Wireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm Wang DaWei and Wang Changliang Zhejiang Industry Polytechnic College

More information

### CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM

CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM *Shabnam Ghasemi 1 and Mohammad Kalantari 2 1 Deparment of Computer Engineering, Islamic Azad University,

More information

### Research on SQLite Database Query Optimization Based on Improved PSO Algorithm

, pp.239-246 http://dx.doi.org/10.14257/ijdta.2016.9.4.22 Research on SQLite Database Query Optimization Based on Improved PSO Algorithm Aite Zhao 1, Zhiqiang Wei 1 and Yongquan Yang 1,* 1 Ocean University

More information

### Genetic Algorithm an Approach to Solve Global Optimization Problems

Genetic Algorithm an Approach to Solve Global Optimization Problems PRATIBHA BAJPAI Amity Institute of Information Technology, Amity University, Lucknow, Uttar Pradesh, India, pratibha_bajpai@rediffmail.com

More information

### Holland s GA Schema Theorem

Holland s GA Schema Theorem v Objective provide a formal model for the effectiveness of the GA search process. v In the following we will first approach the problem through the framework formalized by

More information

### International Journal of Software and Web Sciences (IJSWS) www.iasir.net

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 2279-0063 ISSN (Online): 2279-0071 International

More information

### Genetic Algorithm. Based on Darwinian Paradigm. Intrinsically a robust search and optimization mechanism. Conceptual Algorithm

24 Genetic Algorithm Based on Darwinian Paradigm Reproduction Competition Survive Selection Intrinsically a robust search and optimization mechanism Slide -47 - Conceptual Algorithm Slide -48 - 25 Genetic

More information

### An Improved ACO Algorithm for Multicast Routing

An Improved ACO Algorithm for Multicast Routing Ziqiang Wang and Dexian Zhang School of Information Science and Engineering, Henan University of Technology, Zheng Zhou 450052,China wzqagent@xinhuanet.com

More information

### A Fast Computational Genetic Algorithm for Economic Load Dispatch

A Fast Computational Genetic Algorithm for Economic Load Dispatch M.Sailaja Kumari 1, M.Sydulu 2 Email: 1 Sailaja_matam@Yahoo.com 1, 2 Department of Electrical Engineering National Institute of Technology,

More information

### Research on the Performance Optimization of Hadoop in Big Data Environment

Vol.8, No.5 (015), pp.93-304 http://dx.doi.org/10.1457/idta.015.8.5.6 Research on the Performance Optimization of Hadoop in Big Data Environment Jia Min-Zheng Department of Information Engineering, Beiing

More information

### Using Segment-based Genetic Algorithm with Local Search to Find Approximate Solution for Multi-Stage Supply Chain Network Design Problem

Çankaya University Journal of Science and Engineering Volume 10 (2013), No 2, 185-201. Using Segment-based Genetic Algorithm with Local Search to Find Approximate Solution for Multi-Stage Supply Chain

More information

### Optimization of PID parameters with an improved simplex PSO

Li et al. Journal of Inequalities and Applications (2015) 2015:325 DOI 10.1186/s13660-015-0785-2 R E S E A R C H Open Access Optimization of PID parameters with an improved simplex PSO Ji-min Li 1, Yeong-Cheng

More information

### Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware

Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware Mahyar Shahsavari, Zaid Al-Ars, Koen Bertels,1, Computer Engineering Group, Software & Computer Technology

More information

### Optimal PID Controller Design for AVR System

Tamkang Journal of Science and Engineering, Vol. 2, No. 3, pp. 259 270 (2009) 259 Optimal PID Controller Design for AVR System Ching-Chang Wong*, Shih-An Li and Hou-Yi Wang Department of Electrical Engineering,

More information

### Multiobjective Optimization Applied to the Distribution of Petroleum Products in Pipelines Networks

17 th European Symposium on Computer Aided Process Engineering ESCAPE17 V. Plesu and P.S. Agachi (Editors) 27 Elsevier B.V. All rights reserved. 1 Multiobjective Optimization Applied to the Distribution

More information

### Architectural Design for Space Layout by Genetic Algorithms

Architectural Design for Space Layout by Genetic Algorithms Özer Ciftcioglu, Sanja Durmisevic and I. Sevil Sariyildiz Delft University of Technology, Faculty of Architecture Building Technology, 2628 CR

More information

### Load balancing in a heterogeneous computer system by self-organizing Kohonen network

Bull. Nov. Comp. Center, Comp. Science, 25 (2006), 69 74 c 2006 NCC Publisher Load balancing in a heterogeneous computer system by self-organizing Kohonen network Mikhail S. Tarkov, Yakov S. Bezrukov Abstract.

More information

### Electric Distribution Network Multi objective Design Using Problem Specific Genetic Algorithm

Electric Distribution Network Multi objective Design Using Problem Specific Genetic Algorithm 1 Parita Vinodbhai Desai, 2 Jignesh Patel, 3 Sangeeta Jagdish Gurjar 1 Department of Electrical Engineering,

More information

### Introduction To Genetic Algorithms

1 Introduction To Genetic Algorithms Dr. Rajib Kumar Bhattacharjya Department of Civil Engineering IIT Guwahati Email: rkbc@iitg.ernet.in References 2 D. E. Goldberg, Genetic Algorithm In Search, Optimization

More information

### A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm

Journal of Information & Computational Science 9: 16 (2012) 4801 4809 Available at http://www.joics.com A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm

More information

### A Multi-Objective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms

A Multi-Objective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms MIGUEL CAMELO, YEZID DONOSO, HAROLD CASTRO Systems and Computer Engineering Department Universidad de los

More information

### Department of Industrial Engineering

Department of Industrial Engineering Master of Engineering Program in Industrial Engineering (International Program) M.Eng. (Industrial Engineering) Plan A Option 2: Total credits required: minimum 39

More information

### Towards Heuristic Web Services Composition Using Immune Algorithm

Towards Heuristic Web Services Composition Using Immune Algorithm Jiuyun Xu School of Computer & Communication Engineering China University of Petroleum xujiuyun@ieee.org Stephan Reiff-Marganiec Department

More information

### American International Journal of Research in Science, Technology, Engineering & Mathematics

American International Journal of Research in Science, Technology, Engineering & Mathematics Available online at http://www.iasir.net ISSN (Print): 2328-349, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629

More information

### Design call center management system of e-commerce based on BP neural network and multifractal

Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2014, 6(6):951-956 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 Design call center management system of e-commerce

More information

### Optimizing CPU Scheduling Problem using Genetic Algorithms

Optimizing CPU Scheduling Problem using Genetic Algorithms Anu Taneja Amit Kumar Computer Science Department Hindu College of Engineering, Sonepat (MDU) anutaneja16@gmail.com amitkumar.cs08@pec.edu.in

More information

### DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES

DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES Vijayalakshmi Mahanra Rao 1, Yashwant Prasad Singh 2 Multimedia University, Cyberjaya, MALAYSIA 1 lakshmi.mahanra@gmail.com

More information

### A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster

, pp.11-20 http://dx.doi.org/10.14257/ ijgdc.2014.7.2.02 A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster Kehe Wu 1, Long Chen 2, Shichao Ye 2 and Yi Li 2 1 Beijing

More information

### A Hybrid Tabu Search Method for Assembly Line Balancing

Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization, Beijing, China, September 15-17, 2007 443 A Hybrid Tabu Search Method for Assembly Line Balancing SUPAPORN

More information

### An Efficient Approach for Task Scheduling Based on Multi-Objective Genetic Algorithm in Cloud Computing Environment

IJCSC VOLUME 5 NUMBER 2 JULY-SEPT 2014 PP. 110-115 ISSN-0973-7391 An Efficient Approach for Task Scheduling Based on Multi-Objective Genetic Algorithm in Cloud Computing Environment 1 Sourabh Budhiraja,

More information

### Self-Learning Genetic Algorithm for a Timetabling Problem with Fuzzy Constraints

Self-Learning Genetic Algorithm for a Timetabling Problem with Fuzzy Constraints Radomír Perzina, Jaroslav Ramík perzina(ramik)@opf.slu.cz Centre of excellence IT4Innovations Division of the University

More information

### An Interactive Visualization Tool for the Analysis of Multi-Objective Embedded Systems Design Space Exploration

An Interactive Visualization Tool for the Analysis of Multi-Objective Embedded Systems Design Space Exploration Toktam Taghavi, Andy D. Pimentel Computer Systems Architecture Group, Informatics Institute

More information

### APPLICATION OF ADVANCED SEARCH- METHODS FOR AUTOMOTIVE DATA-BUS SYSTEM SIGNAL INTEGRITY OPTIMIZATION

APPLICATION OF ADVANCED SEARCH- METHODS FOR AUTOMOTIVE DATA-BUS SYSTEM SIGNAL INTEGRITY OPTIMIZATION Harald Günther 1, Stephan Frei 1, Thomas Wenzel, Wolfgang Mickisch 1 Technische Universität Dortmund,

More information

### MQOP - A Tiny Reference to the Multiple- Query Optimization Problem

MQOP - A Tiny Reference to the Multiple- Query Optimization Problem MQOP - Una pequeña referencia al problema de optimización de Múltiple-Query Juan Felipe García* Abstract The multiple query optimization

More information

### A Non-Linear Schema Theorem for Genetic Algorithms

A Non-Linear Schema Theorem for Genetic Algorithms William A Greene Computer Science Department University of New Orleans New Orleans, LA 70148 bill@csunoedu 504-280-6755 Abstract We generalize Holland

More information

### Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling

Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling R.G. Babukartik 1, P. Dhavachelvan 1 1 Department of Computer Science, Pondicherry University, Pondicherry, India {r.g.babukarthik,

More information

### AN APPROACH FOR SOFTWARE TEST CASE SELECTION USING HYBRID PSO

INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 AN APPROACH FOR SOFTWARE TEST CASE SELECTION USING HYBRID PSO 1 Preeti Bala Thakur, 2 Prof. Toran Verma 1 Dept. of

More information

### An ACO Approach to Solve a Variant of TSP

An ACO Approach to Solve a Variant of TSP Bharat V. Chawda, Nitesh M. Sureja Abstract This study is an investigation on the application of Ant Colony Optimization to a variant of TSP. This paper presents

More information

### Optimum Design of Worm Gears with Multiple Computer Aided Techniques

Copyright c 2008 ICCES ICCES, vol.6, no.4, pp.221-227 Optimum Design of Worm Gears with Multiple Computer Aided Techniques Daizhong Su 1 and Wenjie Peng 2 Summary Finite element analysis (FEA) has proved

More information

### Query Optimization by Genetic Algorithm

JOURNAL OF INFORMATION TECHNOLOGY AND ENGINEERING Vol.3 No.1 Jan-June 2012 pp. 44-51 ISSN: 2229-7421 International Science Press www.ispjournals.com Query Optimization by Genetic Algorithm Dr. P.K.Butey

More information

### A Robust Method for Solving Transcendental Equations

www.ijcsi.org 413 A Robust Method for Solving Transcendental Equations Md. Golam Moazzam, Amita Chakraborty and Md. Al-Amin Bhuiyan Department of Computer Science and Engineering, Jahangirnagar University,

More information

### Neural Network and Genetic Algorithm Based Trading Systems. Donn S. Fishbein, MD, PhD Neuroquant.com

Neural Network and Genetic Algorithm Based Trading Systems Donn S. Fishbein, MD, PhD Neuroquant.com Consider the challenge of constructing a financial market trading system using commonly available technical

More information

### Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm

Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm Martin Hlosta, Rostislav Stríž, Jan Kupčík, Jaroslav Zendulka, and Tomáš Hruška A. Imbalanced Data Classification

More information

### Alpha Cut based Novel Selection for Genetic Algorithm

Alpha Cut based Novel for Genetic Algorithm Rakesh Kumar Professor Girdhar Gopal Research Scholar Rajesh Kumar Assistant Professor ABSTRACT Genetic algorithm (GA) has several genetic operators that can

More information

### Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects

Journal of Computer Science 2 (2): 118-123, 2006 ISSN 1549-3636 2006 Science Publications Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects Alaa F. Sheta Computers

More information

### A Comparison of General Approaches to Multiprocessor Scheduling

A Comparison of General Approaches to Multiprocessor Scheduling Jing-Chiou Liou AT&T Laboratories Middletown, NJ 0778, USA jing@jolt.mt.att.com Michael A. Palis Department of Computer Science Rutgers University

More information

### On the Hardness of Topology Inference

On the Hardness of Topology Inference H. B. Acharya 1 and M. G. Gouda 2 1 The University of Texas at Austin, USA acharya@cs.utexas.edu 2 The National Science Foundation, USA mgouda@nsf.gov Abstract. Many

More information

### The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network

, pp.67-76 http://dx.doi.org/10.14257/ijdta.2016.9.1.06 The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network Lihua Yang and Baolin Li* School of Economics and

More information

### Solving Timetable Scheduling Problem by Using Genetic Algorithms

Solving Timetable Scheduling Problem by Using Genetic Algorithms Branimir Sigl, Marin Golub, Vedran Mornar Faculty of Electrical Engineering and Computing, University of Zagreb Unska 3, 1 Zagreb, Croatia

More information

### International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) International Journal of Emerging Technologies in Computational

More information

### SOFTWARE TESTING STRATEGY APPROACH ON SOURCE CODE APPLYING CONDITIONAL COVERAGE METHOD

SOFTWARE TESTING STRATEGY APPROACH ON SOURCE CODE APPLYING CONDITIONAL COVERAGE METHOD Jaya Srivastaval 1 and Twinkle Dwivedi 2 1 Department of Computer Science & Engineering, Shri Ramswaroop Memorial

More information

### D A T A M I N I N G C L A S S I F I C A T I O N

D A T A M I N I N G C L A S S I F I C A T I O N FABRICIO VOZNIKA LEO NARDO VIA NA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe.

More information

### Empirically Identifying the Best Genetic Algorithm for Covering Array Generation

Empirically Identifying the Best Genetic Algorithm for Covering Array Generation Liang Yalan 1, Changhai Nie 1, Jonathan M. Kauffman 2, Gregory M. Kapfhammer 2, Hareton Leung 3 1 Department of Computer

More information

### STUDY ON APPLICATION OF GENETIC ALGORITHM IN CONSTRUCTION RESOURCE LEVELLING

STUDY ON APPLICATION OF GENETIC ALGORITHM IN CONSTRUCTION RESOURCE LEVELLING N.Satheesh Kumar 1,R.Raj Kumar 2 PG Student, Department of Civil Engineering, Kongu Engineering College, Perundurai, Tamilnadu,India

More information

### Bachelor of Games and Virtual Worlds (Programming) Subject and Course Summaries

First Semester Development 1A On completion of this subject students will be able to apply basic programming and problem solving skills in a 3 rd generation object-oriented programming language (such as

More information

### Highway Maintenance Scheduling Using Genetic Algorithm with Microscopic Traffic Simulation

Wang, Cheu and Fwa 1 Word Count: 6955 Highway Maintenance Scheduling Using Genetic Algorithm with Microscopic Traffic Simulation Ying Wang Research Scholar Department of Civil Engineering National University

More information

### Binary vs Analogue Path Monitoring in IP Networks

Binary vs Analogue Path Monitoring in IP Networks Hung X. Nguyen and Patrick Thiran School of Computer and Communication Sciences, EPFL CH-1015 Lausanne, Switzerland {hung.nguyen, patrick.thiran}@epfl.ch

More information

### Effective Estimation Software cost using Test Generations

Asia-pacific Journal of Multimedia Services Convergence with Art, Humanities and Sociology Vol.1, No.1 (2011), pp. 1-10 http://dx.doi.org/10.14257/ajmscahs.2011.06.01 Effective Estimation Software cost

More information

### Multi-objective Traffic Engineering for Data Center Networks

Noname manuscript No. (will be inserted by the editor) Multi-objective Traffic Engineering for Data Center Networks Trong-Viet Ho Yves Deville Olivier Bonaventure Received: date / Accepted: date Abstract

More information

### Load Balanced Optical-Network-Unit (ONU) Placement Algorithm in Wireless-Optical Broadband Access Networks

Load Balanced Optical-Network-Unit (ONU Placement Algorithm in Wireless-Optical Broadband Access Networks Bing Li, Yejun Liu, and Lei Guo Abstract With the broadband services increasing, such as video

More information

### International Journal of Software and Web Sciences (IJSWS) Web Log Mining Based on Improved FCM Algorithm using Multiobjective

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 2279-0063 ISSN (Online): 2279-0071 International

More information

### MULTISTAGE INTERCONNECTION NETWORKS: A TRANSITION TO OPTICAL

MULTISTAGE INTERCONNECTION NETWORKS: A TRANSITION TO OPTICAL Sandeep Kumar 1, Arpit Kumar 2 1 Sekhawati Engg. College, Dundlod, Dist. - Jhunjhunu (Raj.), 1987san@gmail.com, 2 KIIT, Gurgaon (HR.), Abstract

More information

### Genetic algorithms for solving portfolio allocation models based on relative-entropy, mean and variance

Journal of Scientific Research and Development 2 (12): 7-12, 2015 Available online at www.jsrad.org ISSN 1115-7569 2015 JSRAD Genetic algorithms for solving portfolio allocation models based on relative-entropy,

More information

### Proposal and Analysis of Stock Trading System Using Genetic Algorithm and Stock Back Test System

Proposal and Analysis of Stock Trading System Using Genetic Algorithm and Stock Back Test System Abstract: In recent years, many brokerage firms and hedge funds use a trading system based on financial

More information

### Analysis of an Artificial Hormone System (Extended abstract)

c 2013. This is the author s version of the work. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purpose or for creating

More information

### Distribution System Reconfiguration for Loss Reduction using Genetic Algorithm

P. Subburaj J. Electrical Systems -4 (006): 98-07 Research Scholar, National Engineering College, K.R.Nagar, Kovilpatti, Tamil Regular paper Nadu, India. subbunec@yahoo.com K. Ramar Professor and Head

More information

### Web Service Selection using Particle Swarm Optimization and Genetic Algorithms

Web Service Selection using Particle Swarm Optimization and Genetic Algorithms Simone A. Ludwig Department of Computer Science North Dakota State University Fargo, ND, USA simone.ludwig@ndsu.edu Thomas

More information

### D-optimal plans in observational studies

D-optimal plans in observational studies Constanze Pumplün Stefan Rüping Katharina Morik Claus Weihs October 11, 2005 Abstract This paper investigates the use of Design of Experiments in observational

More information

### Effects of node buffer and capacity on network traffic

Chin. Phys. B Vol. 21, No. 9 (212) 9892 Effects of node buffer and capacity on network traffic Ling Xiang( 凌 翔 ) a), Hu Mao-Bin( 胡 茂 彬 ) b), and Ding Jian-Xun( 丁 建 勋 ) a) a) School of Transportation Engineering,

More information

### Detecting Multiple Selfish Attack Nodes Using Replica Allocation in Cognitive Radio Ad-Hoc Networks

Detecting Multiple Selfish Attack Nodes Using Replica Allocation in Cognitive Radio Ad-Hoc Networks Kiruthiga S PG student, Coimbatore Institute of Engineering and Technology Anna University, Chennai,

More information

### Research on the UHF RFID Channel Coding Technology based on Simulink

Vol. 6, No. 7, 015 Research on the UHF RFID Channel Coding Technology based on Simulink Changzhi Wang Shanghai 0160, China Zhicai Shi* Shanghai 0160, China Dai Jian Shanghai 0160, China Li Meng Shanghai

More information

### Power Efficiency Metrics for Geographical Routing In Multihop Wireless Networks

Power Efficiency Metrics for Geographical Routing In Multihop Wireless Networks Gowthami.A, Lavanya.R Abstract - A number of energy-aware routing protocols are proposed to provide the energy efficiency

More information

### An evolutionary learning spam filter system

An evolutionary learning spam filter system Catalin Stoean 1, Ruxandra Gorunescu 2, Mike Preuss 3, D. Dumitrescu 4 1 University of Craiova, Romania, catalin.stoean@inf.ucv.ro 2 University of Craiova, Romania,

More information

### The Application Research of Ant Colony Algorithm in Search Engine Jian Lan Liu1, a, Li Zhu2,b

3rd International Conference on Materials Engineering, Manufacturing Technology and Control (ICMEMTC 2016) The Application Research of Ant Colony Algorithm in Search Engine Jian Lan Liu1, a, Li Zhu2,b

More information

### A Study of Optical Design of Miniature Zoom Optics with Liquid Lenses

Progress In Electromagnetics Research Symposium Proceedings, Suzhou, China, Sept. 12 16, 2011 397 A Study of Optical Design of Miniature Zoom Optics with Liquid Lenses Cheng-Mu Tsai 1, Yi-Chin Fang 2,

More information

### An Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks

An Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks Ayon Chakraborty 1, Swarup Kumar Mitra 2, and M.K. Naskar 3 1 Department of CSE, Jadavpur University, Kolkata, India 2 Department of

More information

### QoS EVALUATION OF CLOUD SERVICE ARCHITECTURE BASED ON ANP

QoS EVALUATION OF CLOUD SERVICE ARCHITECTURE BASED ON ANP Mingzhe Wang School of Automation Huazhong University of Science and Technology Wuhan 430074, P.R.China E-mail: mingzhew@gmail.com Yu Liu School

More information

### HYBRID ACO-IWD OPTIMIZATION ALGORITHM FOR MINIMIZING WEIGHTED FLOWTIME IN CLOUD-BASED PARAMETER SWEEP EXPERIMENTS

HYBRID ACO-IWD OPTIMIZATION ALGORITHM FOR MINIMIZING WEIGHTED FLOWTIME IN CLOUD-BASED PARAMETER SWEEP EXPERIMENTS R. Angel Preethima 1, Margret Johnson 2 1 Student, Computer Science and Engineering, Karunya

More information

### A Genetic Algorithm-Evolved 3D Point Cloud Descriptor

A Genetic Algorithm-Evolved 3D Point Cloud Descriptor Dominik Wȩgrzyn and Luís A. Alexandre IT - Instituto de Telecomunicações Dept. of Computer Science, Univ. Beira Interior, 6200-001 Covilhã, Portugal

More information

### An Evolutionary Algorithm in Grid Scheduling by multiobjective Optimization using variants of NSGA

International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012 1 An Evolutionary Algorithm in Grid Scheduling by multiobjective Optimization using variants of NSGA Shahista

More information

### A Review And Evaluations Of Shortest Path Algorithms

A Review And Evaluations Of Shortest Path Algorithms Kairanbay Magzhan, Hajar Mat Jani Abstract: Nowadays, in computer networks, the routing is based on the shortest path problem. This will help in minimizing

More information

### ARTIFICIAL INTELLIGENCE METHODS IN EARLY MANUFACTURING TIME ESTIMATION

1 ARTIFICIAL INTELLIGENCE METHODS IN EARLY MANUFACTURING TIME ESTIMATION B. Mikó PhD, Z-Form Tool Manufacturing and Application Ltd H-1082. Budapest, Asztalos S. u 4. Tel: (1) 477 1016, e-mail: miko@manuf.bme.hu

More information

### A Reactive Tabu Search for Service Restoration in Electric Power Distribution Systems

IEEE International Conference on Evolutionary Computation May 4-11 1998, Anchorage, Alaska A Reactive Tabu Search for Service Restoration in Electric Power Distribution Systems Sakae Toune, Hiroyuki Fudo,

More information

### Practical Applications of Evolutionary Computation to Financial Engineering

Hitoshi Iba and Claus C. Aranha Practical Applications of Evolutionary Computation to Financial Engineering Robust Techniques for Forecasting, Trading and Hedging 4Q Springer Contents 1 Introduction to

More information

### Evolutionary Detection of Rules for Text Categorization. Application to Spam Filtering

Advances in Intelligent Systems and Technologies Proceedings ECIT2004 - Third European Conference on Intelligent Systems and Technologies Iasi, Romania, July 21-23, 2004 Evolutionary Detection of Rules

More information

### Collapse by Cascading Failures in Hybrid Attacked Regional Internet

Collapse by Cascading Failures in Hybrid Attacked Regional Internet Ye Xu and Zhuo Wang College of Information Science and Engineering, Shenyang Ligong University, Shenyang China xuy.mail@gmail.com Abstract

More information