Solving traveling salesman problem using hybridization of rider optimization and spotted hyena optimization algorithm

Abstract

Traveling Salesman Problem (TSP) is the combinatorial optimization problem, where a salesman starting from a home city travels all the other cities and returns to the home city in the shortest possible path. TSP is a popular problem because the instances of TSP can be applied to solve real-world problems, the implication of which turns TSP into a typical test bench for performance evaluation of novel algorithms. In the current years, different optimization algorithms inspired by biological groups have become very familiar. A combined intelligence of diverse social insects like bees, ants, birds, termites, fish, etc. has been analyzed to introduce multiple meta-heuristic algorithms in the field of swarm intelligence. The main intent of this paper is to develop a hybrid algorithm for solving the TSP robustly and effectively. In order to attain this challenging point, the objective model considered in this research work is the minimization of the distance of the salesman traveling through entire cities. Here, the optimal solution pertains to solve the TSP is to minimize the distance travelled by the salesman, which is determined based on the new hybrid optimization algorithm. This proposal plans to integrate the two well-performing optimization algorithms like Rider Optimization Algorithm (ROA) and Spotted Hyena Optimizer algorithm (SHO) to frame the new algorithm, Spotted Hyena-based Rider Optimization (S-ROA). Finally, the experimental results obtained by the hybrid algorithm to solve these TSP cases are benchmarked against the results obtained by using state-of-the-art algorithms and prove the competitive performance of the proposed model.