A Modified Ant Colony Optimization Algorithm with Pheromone Mutations for Dynamic Travelling Salesman Problem

AbstractAnt Colony Optimization (ACO) is an algorithm based on Meta-heuristics and swarm intelligence, taking inspiration from the exploring behavior of social insects like ants. Some very challenging optimization problems, like the vehicle routing problem, have been solved by Ant colony optimization. But, the Standard ACO algorithm has problems like trapping in local optimum. This paper discusses an improved version of the ACO algorithm, known as Modified ACO (M-ACO). The main motivation of the proposed algorithm is to use pheromone mutation techniques, such as swap and random, in dynamic environments, to overcome the problems of standard ACO and solve the Travelling Salesman Problem, which is a well-known NP-hard problem. Mutation allows better searching and exploring in other areas, for better results. Also, the exploitation is increased that uses local information to search local regions more intensively for finding minimum distance and increasing the rate of convergence. Evaporation-based Ant colony optimization (E-ACO) framework and population-based ant colony optimization (P-ACO) framework are the two groups into which the algorithms under study are separated. Both frameworks are evaluated on dynamic environments produced using TSPLIB base instances and updated for pheromone values using mutation technique. Experiments and Statistical tests were conducted on the TSPLIB datasets such as pr439 and lin318, in dynamic environments and on comparing ACO with M-ACO and P-ACO, better results were obtained for M-ACO and P-ACO. Metrics like P¯offline have been considered to measure algorithm performance and behavior.KEYWORDS: Ant colony optimizationDynamic travelling salesman problemPheromone mutationRandom mutationSwap mutation Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsLavika GoelLavika Goel is currently an assistant professor in the Dept of CSE at NIT, Jaipur. She earlier worked at BITS, Pilani during 2015-2020. She has worked at Oracle India Private Ltd. Goel has published around 72 research papers in SCI-indexed International conferences, journals and book chapters including Elsevier, IEEE, T&F, Springer, etc. She has written a text book on Artificial Intelligence for UG students which was published in January 2021 byWiley Publications and is among the top 10 bestseller books globally on Amazon website to date. She received, in August 2019 and in October 2022, external fundings of Rs. 40 lakhs and Rs. 30 lakhs, respectively as Co-Principal Investigator (Co- PI) and PI from DST, ICPS division and SERB division for her novel ideas of using hybrid ML/Nature-inspired heuristics for developing a question answering system and the other for the design of a crop recommendation system. She is also a life member of IEI India Ltd. and Machine intelligence & Research labs, USA and a Senior member of IEEE. She was awarded the prestigious “Young Scientist award” by VIFRA International Foundation in 2015. She is also the Joint Winner of the IEEE India “Best Women Professional” Award given by IEEE India Council, 2021. This achievement of hers got featured as an article in the Dainik Bhaskar newspaper on 20th Jan 2022.Giriraj VaishnavGiriraj Vaishnav received his bachelor of technology in computer science & engineering from Rajasthan Technical University, Kota, India in 2019 and master of technology in computer science from Central University of Rajasthan, Ajmer, India in 2022. He has qualified for the Graduate Aptitude Test in Engineering (GATE) Exam. He is currently pursuing doctor of philosophy in the Department of Computer Science and Engineering at Malaviya National Institute of Technology (MNIT), Jaipur, India. His areas of interest are nature inspired optimization, machine learning, deep learning, computer vision, and cyber physical systemEmail: 2022rcp9030@mnit.ac.inSiddharth Chand RamolaSiddharth Ramola received his bachelor of technology in computer science & engineering from Institute of Engineering and Management, Kolkata, India in 2020. He has qualified for the Graduate Aptitude Test in Engineering (GATE) Exam. Hehas worked at Tata consultancy services for 2 years. He is currently pursuing a master of technology in the Department of Computer Science and Engineering at Malaviya National Institute of Technology (MNIT), Jaipur, India. His areas of interest are nature inspired optimization algorithms, databases, system design, and programming.Email: 2022pcp5438@mnit.ac.inTushar PurohitTushar Purohit received his bachelor of technology in computer science & engineering from Uttaranchal Institute of Technology, Dehradun, India in 2013. He has qualified for the Graduate Aptitude Test in Engineering (GATE) Exam. He is currently pursuing a master of technology in the Department of Computer Science and Engineering at Malaviya National Institute of Technology (MNIT), Jaipur, India. His areas of interests are network and information security, nature inspired optimization algorithms, and machine learning.Email: 2020pcp5524@mnit.ac.in