Particle Swarm Optimisation Utilising Adaptive Fitness Function Generation

Adam Klyne

Abstract


EDITOR: This paper is under review in an external journal. When accepted we will redirect to the published article.

The contribution of this paper is an algorithm for integrating an anomaly detector with particle swarm optimisation. We do so in order to achieve task allocation of resources when the signature of a task is not well understood in advance. For our proposed system we first discuss two candidate anomaly detection approaches; a single layer feed forward neural network and background subtraction. Once tasks are identified we then allow a swarm intelligence system to autonomously allocate resources to them. We found that both techniques work well for detecting scene anomalies, with the neural network being more tolerant to minor aberrations in the input data. In situations where the input is tightly controlled we found that the background subtraction method provides superior performance.

Keywords


Particle Swarm Optimisation; Anomaly Detection; Optimisation; Neural Network; Clustering; Background Subtraction