Hybrid Arithmetic-Rider Optimization algorithm as new intelligent model for travelling salesman problem

Travelling Salesman Problem (TSP) is one of the significant NP-hard benchmark problems for performing discrete optimization. In recent times, determining the optimal route mechanism has been implemented and ensured as an important mechanism for solving practical applications. In real-time applications, energy-saving, reaching the destination at the assigned time, and identifying the shortest route is very essential. Route determination is researched by different scientists and engineers. Numerous research is done on this problem, and it is still challenging. Therefore, meta-heuristics are involved in this problem as it is inspired by the biological species. This article constitutes the continuation of the work on adapting the TSP using the hybridized algorithm named Arithmetic-ROA (A-ROA). The ultimate aim of the proposed TSP is to reduce the distance travelled by the salesman while focusing on the entire city. To attain this optimal solution, the novel hybrid A-ROA optimizes the number of the city to be travelled. The results acquired by the new hybrid heuristic are compared with other heuristic algorithms. The computational results confirm that the developed algorithm obtained better results when compared with existing algorithms. The developed optimization algorithm also provides enhanced performance within a realistic amount of computational time.