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Quantum-inspired Particle Swarm Optimisation for Integrated Feature and Parameter Optimisation of Evolving Spiking Neural Networks

Nikola Kasabov, Haza Nuzly Abdull Hamed


The paper deals with feature (variable) and model parameter optimisation utilising a proposed dynamic quantum–inspired particle swarm optimisation method. In this method the features of the model are represented probabilistically as a quantum bit vector and the model parameter values – as real numbers. The principle of quantum superposition is used to accelerate the search for an optimal set of features, that combined through co-evolution with a set of optimised parameter values, will result in an optimal model. The paper applies the method to the problem of feature and parameter optimisation of evolving spiking neural network models. A swarm of particles is used to find the classification model with the best accuracy for a given classification task. The method is illustrated on a bench mark classification problem. The proposed method results in the design of faster and more accurate classification models than the ones optimised with the use of standard evolutionary optimisation algorithms.


Particle Swarm Optimisation, Quantum Computation, Evolutionary Algorithms, Spiking Neural Networks, Classification

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