Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization in Computer Networks Using Metaheuristics
Multi-Objective Optimization in Computer Networks Using Metaheuristics
Critical Information Infrastructure Security
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Computer Networks: The International Journal of Computer and Telecommunications Networking
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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Communications networks have become a key component to all aspects of a modern organization. Network service disruption can compromise normal operation, even it can threaten survivability of business or enterprise. Because of business risk generated by communication lost, organizations have to planning a dependable infrastructure and configure an architecture that guarantees survivability. This work is focused on tasks that a system has to deploy for assuring continuity on its communications by redundancy. This paper affords a point of view for modelling a redundant network based on graphs. This is proposal of using multi objective genetic algorithms for figuring out an optimal configuration of a communication network taking into account two particular objectives. First, it is related to cost to implement and maintains redundant devices on a network. Second, the availability measured in terms of probability of damage and mean time to fail.