In this research, we are studying the possibility of contribution in solving the Vehicle Routing Problem With Time Windows(VRPWTW), that is one of the optimization problems of the NP-hard type. This problem has attracted a lot of attention at the pre
sent time because of its real life applications. However, there is still no algorithm that provides us with the perfect solution to this problem because of the complexity of polynomial time. This means that the time of the solution to the Vehicle VRPWTW is growing steadily with the increase in the number of nodes .All the used algorithms have given solutions that are close to the optimal one . We'll introduce two algorithms , the first is Improved Ant Colony System algorithm (IACS) that is capable of searching multiple search areas simultaneously in the solution space is good in diversification ,and the second Simulated Annealing algorithm (SA) is a local search technique that has been successfully applied to many NP-hard problems. Moreover, we will present the In this research Hybrid algorithm (HA) Hybrid Algorithm provided (IACS-SA) that integrate between improved ant algorithm and Simulated Annealing algorithm . We will known standard tests are given to demonstrate the applicability and efficiency of the presented approach and comparisons with other available results are presented.
We study in this paper the possibility of contribution in solving the vehicle routing problem (VRP) by using the improved ant colony system ( IACS) , which is one of the optimization problems that, because of its Real Life applications, has attracted
a lot of attention at the present time. It is a problem of the NP-hard type. However, because of the complication of polynomial time there is still no algorithm providing us with the optimal solution of this problem. All the used algorithms give solutions that are close to the optimal one .
We present the improved ant colony system algorithm that, based on ant colony system algorithm, possesses a new state transition rule, a new pheromone updating rule and diverse local search approaches .
The experimental results of the proposed ( IACS) algorithm compared with the results of well-known standard tests show that our IACS yields better solutions than the other ant algorithms in the literature and is competitive with other meta-heuristic approaches in terms of quality(run time and number of good solutions ).
