Improving the Performance of Proactive Protocols in Manets Networks Using Artificial Intelligence Algorithms

Mobile Ad-Hoc Networks (MANETs) are infrastructure-less networks that are rapidly deployable and self-configuring and do not need central support. MANETs consist of a group of mobile nodes that act either as a router or as a host. Nodes in these network move rapidly and randomly, causing a continuous change in network topology. The routing in the network and choosing the best path between nodes are major issues that attract the attention of researchers in the field of mobile networks, because of the importance of the routing process and its impact on network performance. This paper focuses on improving the performance of the proactive OLSR protocol in order to choose the best routing path that achieves the least time delay in the network, secures the best packet delivery rate and ensures reducing packet loss during the transmission process. The ant colony algorithm was used to choose the best path based on two main factors , namely the path length and the occupancy of the nodes within the path. Our simulation scenarios are built using NS2.35 to test the performance of the improved protocol in terms of increasing the number of nodes in the network and increasing the speed of nodes in the network. The test results show a reduction in the time delay in the network and an increase in the packet delivery rate.

New Hybrid Evolutionary Algorithm for Solving Multi-Objective Optimization Problems

Multi-objective evolutionary algorithms are used in a wide range of fields to solve the issues of optimization, which require several conflicting objectives to be considered together. Basic evolutionary algorithm algorithms have several drawbacks, such as lack of a good criterion for termination, and lack of evidence of good convergence. A multi-objective hybrid evolutionary algorithm is often used to overcome these defects.

Modified Hybrid Algorithm To Contribute in Solving The Multi- Objective Vehicle Routing Problem With Time windows Based On Pareto Optimization

In this research, we are studying the possibility of contribution in solving the multi-objective vehicle Routing problem with time windows , that is one of the optimization problems of the NP-hard type , This problem has attracted a lot of attention now because of its real life applications. Moreover, We will also introduced an algorithm called hybrid algorithm (HA) which depends on integrates between Multiple objective ant colony optimisation (MOACO) and tabu search (TS) algorithm based on the Pareto optimization , and compare the presented approach is the developer with standard tests to demonstrate the applicability and efficiency.

Comparison between branch and cutting algorithm and ant colony algorithm in order to contribute solving the problem of postman Traveling Salesman Problem

In this research we are studying the possibility of contributing in solving the problem of the Traveling Salesman Problem, which is a problem of the type NP-hard . And there is still no algorithm provides us with the Optimal solution to this problem . All the algorithms used to give solutions which are close to the optimal one .

Improved Ant Colony System Algorithm To Solve The Vehicle Routing Problem

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 ).

Use of hybrid methods to contribute in solving the Vehicle Routing Problem

n this research, we are studying the possibility of contribution in solving the Vehicle Routing Problem (VRP), 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 . In this research, we will present the Hybrid Algorithm (HA) in two phases .In the first phase the Sweep Algorithm (SW) is applied, and in the second one the Ant Colony Algorithm and the local search 3-opt are applied. we will then compare the quality of the solution resulted from this hybrid approach with the results of well-known standard tests to determine the effectiveness of the presented approach .