Experimental analysis of ant system on travelling salesman problem dataset TSPLIB

被引:0
作者
Thirugnanasambandam K. [1 ]
Raghav R.S. [2 ]
Saravanan D. [3 ]
Prabu U. [4 ]
Rajeswari M. [5 ]
机构
[1] Department of Computer Science and Technology, School of Computers, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh
[2] School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu
[3] Department of Computer Science and Engineering, KL Deemed to be University, Guntur, Vaddeswaram, Andhra Pradesh
[4] Department of CSE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada
[5] Sri Manakula Vinayagar Engineering College, Puducherry
关键词
Ant Colony Optimization; Ant System; Travelling Salesman Problem; TSPLIB;
D O I
10.4108/eai.13-7-2018.163092
中图分类号
学科分类号
摘要
INTRODUCTION: Traveling Salesman Problem (TSP) is one of the vast research areas and has been considered as sub problems in many fields apart from computer science and also in the field of computer science. OBJECTIVES: This paper deals with the comparison of Ant System Ant System (AS) which is a variant of Ant Colony Optimization. METHODS: The performance of the Ant System is analysed by applying it on the Travelling Salesman Problem (TSP). The optimal results found on TSP using AS has been analysed with the elapsed time taken to find the optimal results, its mean, median, variance and the standard deviation. RESULTS: And also, the quality of solutions has been made by calculating the percentage of the optimality and the deviation of the solutions from the TSPLIB provides best known solutions. For instances, TSPLIB data sets have been used. CONCLUSION: Totally, 7 instances have been executed with three different set of parameters for AS and the results are analysed in terms of different parameter settings and performance metrics on each of it. The role of parameters has also been discussed along with the experimental results. © 2020 Kalaipriyan Thirugnanasambandam et al.
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共 62 条
[1]  
Dantzig G.B., Ramser J.H., The Truck Dispatching Problem, Management Science, 6, 1, pp. 80-91, (1959)
[2]  
Asaithambi S., Rajappa M., Ravi L., Optimization and control of CMOS analog integrated circuits for cyber-physical systems using hybrid grey wolf optimization algorithm, Journal of Intelligent & Fuzzy Systems, 36, 5, pp. 4235-4245, (2019)
[3]  
Ravi L., Subramaniyaswamy V., Vijayakumar V., Chen S., Karmel A., Devarajan M., Hybrid location-based recommender system for mobility and travel planning, Mobile Networks and Applications, 24, 4, pp. 1226-1239, (2019)
[4]  
Logesh R., Subramaniyaswamy V., Exploring hybrid recommender systems for personalized travel applications, Cognitive Informatics and Soft Computing, pp. 535-544, (2019)
[5]  
Zapfel G., Bogl M., Multi-period vehicle routing and crew scheduling with outsourcing options, International Journal of Production Economics, 113, pp. 980-996, (2008)
[6]  
Holthaus O., Rajendran C., A fast ant-colony algorithm for single-machine scheduling to minimize the sum of weighted tardiness of jobs, Journal of the Operational Research Society, 56, pp. 947-953, (2005)
[7]  
Merkle D., Middendorf M., “An ant algorithm with a new pheromone evaluation rule for total tardiness problems”, Evoworkshops, pp. 287-296, (2000)
[8]  
Arnaout J., Musa R., Rabadi G., Ant colony optimization algorithm to parallel machine scheduling problem with setups”, 4Th IEEE Conference on Auto-Mation Science and Engineering, (2008)
[9]  
Ahmadizar F., Barzinpour F., Arkat “.J., Solving permutation flow shop sequencing using ant colony optimization, 2007 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 753-757, (2007)
[10]  
Marimuthu S., Ponnambalam S.G., Jawahar N., Threshold accepting and ant-colony optimization algorithms for scheduling m-machine flowshops with lot streaming, Journal of Materials Processing Technology, 209, pp. 1026-1041, (2009)