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Due to the disadvantages of the ant algorithm used in the combining optimization in continuous space and the demerit of BP algorithm being vulnerable in the local optimum, the dynamics model of chaos ant colony has been introduced into the optimization of weights in neural network model. Therefore, the chaos ant colony neural network can have both extensive mapping ability of neural network and rapid...
Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been widely used to solve various scientific and engineering problems because of to their simplicity and flexibility. Here we report a novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior. The framework is mainly based on the producer-scrounger...
A mobile ad-hoc network is a set of mobile nodes that communicate with each other through the airwave. These networks have a high flexibility and have no central infrastructure or supervisor. In this type of networks, the topology is in change always, and the important problem in them is finding the path between source and destination. In this paper, we introduce a multi path hybrid routing algorithm...
In this paper, we propose a swarm intelligence based reinforcement learning (SWIRL) method to train artificial neural networks (ANN). Basically, two swarm intelligence based algorithms are combined together to train the ANN models. Ant Colony Optimization (ACO) is applied to select ANN topology, while Particle Swarm Optimization (PSO) is applied to adjust ANN connection weights. To evaluate the performance...
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