Experimental Analysis of Ant System on Travelling Salesman Problem Dataset TSPLIB

Abstract

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.

Heurística de Intercambio 2Opt Best Improvement_r y Nivel de Eficacia de las soluciones del Problema del Agente Viajero Simétrico

“…Esto significa que conforme aumenta el número de nodos aumenta la dificultad para resolverlo. Mientras que Thirugnanasambandam, Raghav, Saravanan, Prabu y Rajeswari [10] señalan que la primera formulación matemática para el TSP fue estudiada en 1930 por Karl Menger y posteriormente Hassler Whitney introdujo el término TSP. Anaya-Fuentes [8] refiere que los primeros en intentar resolverlo fueron Dantzing, Fulkerson y Johnson en 1954 por medio de un algoritmo basado en ramificación y acotamiento percatándose que a medida que crecía el tamaño del problema crecía el tiempo para encontrar la solución, por ello surgieron métodos que encuentran una buena solución, no necesariamente óptima, en un tiempo aceptable.…”

“…al. [10], quienes la utilizaron para solucionar instancias simétricas de TSPLIB de tamaño hasta 100 nodos.…”

Optimal path planning for intelligent automated wheelchair using DDSRPSO

“…In this paper, a robust and efficient swarm intelligence algorithm directional driven selfregulated particle swarm optimization (DDSRPSO) is used to find the dynamic optimal path with a quick response (Thirugnanasambandam et al, 2019b). The proposed method has been simulated in MATLAB with sensors attached for finding the near obstacle, which helps in creating a run time robust path.…”