International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) ISSN 2249-6831 Vol. 3, Issue 2, Jun 2013, 209-212 © TJPRC Pvt. Ltd.

RESOLVING MULTIPLE TRAVELLING SALESMAN PROBLEM USING GENETIC ALGORITHMS HARPREET SINGH1 & RAVREET KAUR2 1

M.Tech Student, Department of Computer Science & Engineering, Guru Nanak Dev University, Amritsar, Punjab, India 2

Lecturer, Department of Computer Science & Engineering, Guru Nanak Dev University, Amritsar, Punjab, India

ABSTRACT This paper deals with Genetic Algorithms and also provide details how Resolving Multiple Travelling Salesman Problem Using Genetic Algorithm. Genetic algorithms are powerful search methods. The multipleTraveling Salesman Problem is a complex combinatorial optimization problem so the Genetic algorithms can apply to solve this problem.

KEYWORDS: Genetic Algorithms, Multiple Travelling Salesman Problem, Decoding INTRODUCTION Genetic algorithms are a relatively new optimization technique. This algorithm based on natural evolution [2]. The operator’s adaptation techniques in Gas can be classified into three categories, population level, individual level, and component level adaptation [1]. At the level of population, parameters are adapted globally by using the feedback information from the current population.[2] The basic explanation is required to understand how genetic algorithm work. The genetic algorithm process consists of the following steps: 

Encoding

Evaluation

Crossover

Mutation

Decoding

Encoding Encoding process is most difficult by using genetic algorithms. The traditional way to represent solution is with string of zeros and ones. The strings are called chromosomes and each element of the string is called a gene. The randomly generate many chromosomes and together they are called the population. Evaluation The use of evaluation function is important to decide how good a chromosome is. Crossover In this process randomly choose two chromosomes to crossover, randomly pick a crossover point, then switch all genes after that point Mutation Due to the randomness of the process will occasionally have chromosomes near a local optimum but none near

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the global optimum. Mutation is a completely random way of getting to possible solutions that would otherwise not be found. It is performed after crossover by randomly choosing a chromosome in the new generation to mutate. Decoding This process is used to convert the into actual form. [6]

Figure 1: Flow Chart of Genetic Algorithm Genetic Algorithm applications[7] Function Optimizer Difficult, discontinuous, multi-modal, noisy functions Combinatorial Optimization Layout of VLSI circuits, factory scheduling, traveling salesman problem Design and Control Bridge structures, neural networks, communication networks design control of chemical plants, pipelines Machine Learning Classification rules, economic modeling, scheduling strategies Portfolio design, optimized trading models, direct marketing models, sequencing of TV advertisements, Adaptive agents, data mining etc. The different application of genetic algorithms is the prisoners dilemma.[3] It is a game where two prisoners are held separate cells and cannot communicate. Each is asked to defect and betray the other and must decide whether to do rather than cooperating with the other prisoner. Various companies have released their versions of desktop search engines like Microsoft Windows desktop search, Yahoo search, Copernican desktop search, Google desktop search, Archivarius 3000. The web creates new challenges for information retrieval. The amount of information and users on the web is growing rapidly. People are likely to surf the web using its link graph, often starting with the high quality human maintained indices such as Rediff or with search engines. Human maintained lists cover popular topics effectively but are subjective, expensive to build and maintain, slow to improve, and cannot cover all esoteric topics, Automated search engines that rely on keyword matching usually return too many low. Work Done So Far In the last few years desktop search packages have been released by many commercial software companies. These

Resolving Multiple Travelling Salesman Problem Using Genetic Algorithms

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packages vary their cost, performance and their look and feel. The performance indexing by performing ranking system and the contents of the URLâ&#x20AC;&#x2122;s are stored and viewed and filtered through the content filter. At present there are two approaches for desktop search: one is the direct search for the file location, and the other is the way of searching files by file name, type, text content. These are related to the basic information and they does not manage a large number of data. Problem Definition The number of files stored in a personal computer is increasing very quickly, so it is difficult for users to find the information they want, At the same time, because the price of storage equipment with large capacity is becoming lower and lower, the number of various documents stored in the personal computer, such as digital photos, text files, video and audio files, increases in an amazing rate.. Literature Survey Chan -Tien Lu et all 2007[8] has evaluated the performance of different search engine and archivarius. The five desktop search engines are evaluated on the following criterion and measures on TREC documents: Recall-precision average, Document-level precision Document-level recall, Mean Average Precision (MAP), exact precision and recall over retrieved set, Document-level relative precision.

Figure 2: Performance of Various Search Engine[8] Herwig Unger, Markus Wulff and Fachbereiech Informatik 2003[9] introduce the main structure and functionality of such a decentralized cooperative search engine. It is working on the basis of so called ants. This search engine reduces the search time for information, which are needed by more than one node in peer -to-peer network community. Therefore, ants are able to switch in a special manner between different behavior strategies to search, concentrate and return the respective information. Alireza Bagheri and Ali mohammad Saghiri 2009[10] has given information about a peer to peer system over the internet to offer common services. Providing architecture for large-scale personalized peer to peer information retrieval e.g. personalized searching of the ubiquitous environment information retrieval considering userâ&#x20AC;&#x2122;s profile is important. The architecture for personalized information retrieval in ubiquitous environment with peer-to-peer systems. Dr. Umesh Sehgal, Ms. Kuljeet Kaur, Mr. Pawan Kumar 2007[11] provides an in-depth description of largescale web search engine to define the values and traditional techniques of data in hypertext. There are new technical challenges involved with using the additional information present in hypertext to produce better search results. Zhiwang Cen, Jungang Xu, Jian Sun 2012[12] composed of four modules including Data crawler, Task scheduler, data indexer and data searcher. The implementations of these four modules are described in details and the implementations of user interface of So Desktop is also introduced.

FUTURE WORK The challenge in the desktop search engine is to collect all data and execute the query input by user with the help of crawler, data indexer, task scheduler and data searcher. Data collection is done by the quantitative analysis of open

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source software projects. This work will increase the searching speed of desktop searching engine through which all the program is executed.

CONCLUSIONS This paper consist with detail of different solution that is provided in different paper and also shows efficient use of genetic algorithms to solve the multiple salesman problem by using Genetic Algorithms.

REFERENCES 1.

Imtiaz Korejo,Shengxiang Yang, and ChangheLi,”A comparative study of adaptive mutation operators for Genetic Algorithms” published in 2009 Humberg, germany.

2.

KylieBryant,ArthurBenjamin,Advisor,”Genetic Algorithms and Traveling Salesman Problem” Published in 2000 Harvey Mudd College.

3.

Andras Kiraly,Jonos Abonyi,”A novel approach to solve multiple traveling salesmen problem by Genetic Algorithms” published in university of Pannonia, Hungary.

4.

R.nallusamy,K.Duraiswamy,R.Dhanalaksmi,P.parthiban,”Optimization of

non-linear multiple traveling

salesman problem using k-means Clustering,shrink wrap algorithm and meta-heuristics” published 2009 in International journal of nonlinear science,vol. 9,pp.171-177. 5.

Tolga Bektas,” The Multiple traveling Salesman problem: an overview of formulations and solution procedures” published 2005 in The International journal of management science.

6.

Vu Duong and Allen R.stobberud,” System identification by Genetic Algorithm”

7.

Dirk Czarnitzki and Thorsten Doherr,”Genetic algorithms: A tool for optimization in econometrics basic concept and example for expirial application”

8.

Chang-Tien Lu, Manu Shukla, Siri H. Subramanya, Yamin Wu , “Performance Evaluation of Desktop Search Engines” published in 2007.

9.

Herwig Unger,Markus Wulff and Fachbereich Informatik Universit¨at Rostock ,”Towards a Decentralized Search Engine for P2P-Network Communities” published in 2003

10. Alireza Bagheri, Ali mohammad Saghiri,”An Adaptive Architecture for Personalized Search Engine in Ubiquitous Environment with Peer to Peer systems” published in 2009. 11. Dr.Umesh Sehgal, Ms.Kuljeet Kaur, Mr.Pawan Kumar ,”The Anatomy of a Large-Scale Hyper Textual Web Search Engine” published in 2007 12. Zhiwang Cen, Jungang Xu, Jian Sun,” SoDesktop: a Desktop Search Engine” published in 2012

AUTHOR ‘S DETAILS

Mr. Harpreet Singh have done B.tech and now he is pursueing M.tech from Department of Computer Science & Engineering Guru Nanak Dev University Amritsar. contact-09780685006 Ms. Ravreet Kaur Lecturer in Department of Computer Science & Engineering Guru Nanak Dev University Amritsar

24 resolving multiple full

This paper deals with Genetic Algorithms and also provide details how Resolving Multiple Travelling Salesman Problem Using Genetic Algorithm...