Global shortest path programming using genetic algorithms

The global shortest path programming (GSPP) has extensive applications in engineering practices. The Steiner tree problem is a nonlinear programming conundrum with fixed points and fictive points and is typical theoretical basis of GSPP. The Steiner minimum tree (SMT) problem can be changed to a combination-optimization problem, a test selection algorithm for the construction of the initial population is proposed correspondingly, and an improved genetic algorithms (GA) is discussed to solve the objective of SMT problem. The simulation shows that the global optimum can be quickly obtained by the improved algorithm. Compared with the visualization experiment approach, the proposed approach can be fulfilled accurately and rapidly and it provides a convenient way and tool for the solution to the practical application problems in engineering fields.