Comparative study between a neural network, approach metaheuristic and exact method for solving Traveling salesman Problem
Safae Rbihou, Khalid Haddouch · 2021
optimization problems currently occupy an important place in the scientific community. Intuitively, an optimization problem can be seen as a search problem that consists in exploring a space containing the set of all feasible solutions, in order to find the optimal solution. The traveling salesman problem (TSP), considered as a classical example of combinatorial optimization problem, is considered as an NP-complete problem. In this work we will divide the solution of combinatorial optimization problems into three classes: continuous Hopfield network (CHN), ant colony optimization (ACO) and exact methods programmed in Cplex. The solution of a CHN optimization problem is based on a certain energy or Lyapunov function, which decreases as the system evolves until it reaches a local minimum value. Ant colony optimization to solve the traveling salesman problem (TSP) is inspired by the foraging behavior of ants. As a special case, and in order to test these methods, some computational experiments solving the TSP are also included.