A Biogeography-Based Optimization with a Greedy Randomized Adaptive Search Procedure and the 2-Opt Algorithm for the Traveling Salesman Problem

We develop a novel method to improve biogeography-based optimization (BBO) for solving the traveling salesman problem (TSP). The improved method is comprised of a greedy randomized adaptive search procedure, the 2-opt algorithm, and G2BBO. The G2BBO formulation is derived and the process flowchart is shown in this article. For solving TSP, G2BBO effectively avoids the local minimum problem and accelerates convergence by optimizing the initial values. To demonstrate, we adopt three public datasets (eil51, eil76, and kroa100) from TSPLIB and compare them with various well-known algorithms. The results of G2BBO as well as the other algorithms perform close enough to the optimal solutions in eil51 and eil76 where simple TSP coordinates are considered. In the case of kroa100, with more complicated coordinates, G2BBO shows greater performance over other methods.

Automated summary

We propose a novel method to enhance biogeography-based optimization (BBO) for solving the traveling salesman problem (TSP). The method combines a greedy randomized adaptive search procedure, the 2-opt algorithm, and G2BBO. G2BBO effectively tackles the local minimum problem and improves convergence by optimizing initial values. Experimental results on three TSP datasets demonstrate that G2BBO performs competitively against other well-known algorithms, particularly in cases with complex coordinates.