An improved chemical reaction optimization algorithm for solving traveling salesman problem

Traveling Salesman Problem (TSP) is a well-known optimization problem which tries to find the shortest path between numbers of cities. Several approaches in literature are proposed to solve TSP in a reasonable time. Meta-heuristic Optimization techniques considered as one of these approaches such as Chemical Reaction Optimization (CRO), which is a recently established meta-heuristic algorithm for solving optimization problems. CRO proved its success in solving many optimization problems. In this paper, we present a hybrid algorithm based on CRO and Greedy algorithms for solving the TSP problem called (GCRO). We first introduce a solution using CRO. Then we enhance the solution by hybridizing it with a greedy strategy. And then conduct a performance comparison between the CRO, GCRO and Genetic Algorithm (GA) in terms of error rate and execution time. Better results are obtained using the hybrid model GCRO.