The broadcast storm problem in a mobile ad hoc network
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Comparison of broadcasting techniques for mobile ad hoc networks
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Improving flexibility and efficiency by adding parallelism to genetic algorithms
Statistics and Computing
Fine-Grained Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Cellular Evolutionary Algorithms: Evaluating the Influence of Ratio
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Comparing Synchronous and Asynchronous Cellular Genetic Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Selection intensity in asynchronous cellular evolutionary algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Optimal broadcasting in metropolitan MANETs using multiobjective scatter search
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
| Hi-index | 0.00 |
Mobile Ad-hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such scenario, broadcasting becomes an operation of capital importance for the own existence and operation of the network. Optimizing a broadcast strategy in MANETs is a multi-objective problem accounting for three goals: reaching as many stations as possible, minimizing the network utilization, and reducing the makespan. In this paper, we study the fine-tuning of broadcast strategies by using a cellular multi-objective genetic algorithm (cMOGA) that computes a Pareto front of the solutions to empower a human designer with the ability of choosing the preferred configuration for the network. We define two formulations of the problem, one with three objectives and another one with two objectives plus a constraint. Our experiments using a complex and realistic MANET simulator reveal that using cMOGA is a promising approach to solve the optimum broadcast problem.