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The paper deals with a modification in the learning phase of AntNet routing algorithm, which improves the system adaptability in the presence of undesirable events. Unlike most of the ACO algorithms which consider reward-inaction reinforcement learning, the proposed strategy considers both reward and penalty onto the action probabilities. As simulation results show, considering penalty in AntNet routing...
In this paper we focus on linearity and nonlinearity of learning schemes applied in ant colony optimization algorithms and discuss about the consequences of the two approaches on the overall algorithm's performance and efficiency. The paper reviews the previously proposed ACO algorithms, talking about the underlying linear philosophy of most of them, and proposes a nonlinear learning scheme by which...
The paper deals with a conceptual modification on the learning phase of AntNet routing algorithm through nonlinear reinforcement. Since the learning structure of AntNet consists of colonies of learning automata, the proposed approach replaces the previously defined linear learning automata structure with nonlinear learning automata, which modifies the reinforcement process without imposing overhead...
The paper describes a novel method to introduce new concepts in functional and conceptual dimensions of routing algorithms in swarm-based communication networks.The method uses a fuzzy reinforcement factor in the learning phase of the system and a dynamic traffic monitor to analyze and control the changing network conditions.The combination of the mentioned approaches not only improves the routing...
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