Development of Deer Hunting linked Earthworm Optimization Algorithm for solving large scale Traveling Salesman Problem

摘要

Traveling Salesman Problem (TSP) has been seen in diverse applications, which is proven to be NP-complete in most cases. Even though there are multiple heuristic techniques, the problem is still a complex combinatorial optimization problem. The candidate solutions are chosen by considering only a set of high values of the objective function which may not lead to the best solutions. Hence, this paper develops a hybrid optimization algorithm, named Earthworm-based DHOA (EW-DHOA) to solve the TSP problem by finding an optimal solution. The proposed EW-DHOA is developed by integrating the two well-performing meta-heuristic algorithms, such as Deer Hunting Optimization Algorithm (DHOA) and Earthworm Optimization Algorithm (EWA). The EW-DHOA intends to optimize the constraint as the number of cities traveled by the salesman in terms of an optimal path. The main process for attaining this objective is to minimize the distance traveled by the salesman concerning the entire cities. The effectiveness of the proposed hybrid meta-heuristic algorithm is validated over the benchmark dataset. Finally, the experimental results show that the convergence of the proposed hybrid optimization will be better while solving TSP with less computational complexity, and improved significantly in attaining optimal results.