Saurabh Ranjan Srivastava

Papers

Total Citations

H-Index

About

Saurabh Ranjan Srivastava is a computer scientist whose research focuses on solving complex optimization problems, particularly NP-complete challenges, through advanced metaheuristic algorithms. His most cited work, "Solving Travelling Salesman Problem Using Improved Genetic Algorithm" (2017, 6 citations), addresses one of the most notoriously difficult problems in computational theory—the Travelling Salesman Problem (TSP). Srivastava’s key contribution lies in enhancing traditional genetic algorithms to more effectively tackle NP-complete problems, which have widespread applications in science and engineering, from logistics to circuit design. By demonstrating that these problems are not solvable through conventional algorithmic approaches alone, his work underscores the critical role of evolutionary computation in modern optimization. While his citation count is modest, the practical significance of his research resonates with students and researchers seeking efficient solutions to real-world routing and scheduling challenges. Srivastava’s work serves as a valuable entry point for those exploring the intersection of artificial intelligence and combinatorial optimization, highlighting how improved genetic algorithms can push the boundaries of what is computationally feasible.