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@article{162229,
        author = {Dipanwita Saha},
        title = {A Survey Report on “Optimization of different variants of Travelling Salesman Problem”},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {8},
        pages = {423-432},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162229},
        abstract = {We have covered a few optimizations of various Traveling Salesman Problem (TSP) variations in this paper, along with Ant Colony Optimization (ACO)-based methods for TSP. Techniques for ant colony optimization were created for static optimization issues, where the input data is predetermined and does not change over time. Among the significant suggestions of this kind are various modifications to the ACO algorithm to improve information reuse and a population-based ACO algorithm created especially for dynamic combinatorial optimization problems. We cover the ACO algorithm for a time-dependent traveling salesman problem, hybrid ACO for a solid multiple traveling salesman problem, and ACO for a multi-conveyance TSP with a cost and time limit among these. An extensive survey of ACO-based solutions for TSP issues is presented in this paper. The experimental section presents computational results using various input data sets.},
        keywords = {Travelling Salesman Problem, Multi-objective, Genetic Algorithm, Ant Colony Optimization.},
        month = {},
        }