In this paper, we present a new form of particle swarm optimisation (PSO) in which each particle uses an artificial neural network (ANN) to guide its movements. Information about each of the particle's informants is passed as input to the ANN and the ANN's outputs are then used to select which informant to follow at the next iteration. Using a distributed evolutionary process, each particle's ANN is able to learn about the solution landscape over the course of an optimisation run, potentially allowing the particle to avoid unfavourable regions. An initial evaluation of this approach using a suite of 5 continuous optimisation functions suggests that it improves performance, managing to get consistently closer to the global optima than conventional PSO on all of these problems. An analysis of the trajectories indicates that the behaviour of the algorithm is quite different to conventional PSO, with a much higher degree of exploration than the baseline PSO algorithm.
|Title of host publication||2020 IEEE Congress on Evolutionary Computation (CEC)|
|Publication status||Accepted/In press - 20 Mar 2020|