Particle Swarm Optimisation with Enhanced Memory Particles

Particle Swarm Optimisation (PSO) is a Swarm Intelligence based optimisation algorithm. The algorithm consists of a population of individuals that cooperate to find the global optimum of a search space. The individual particles in the swarm move according to two influences; self-cognition and social emulation. Particles wish to return to their own previous successes and also copy the behaviour of other successful particles. PSO is simple, fast and robust which makes it ideal for many optimisation problems.This thesis introduces a new variant of the PSO algorithm, PSO with Enhanced Memory Particles, where the cognitive influence on particles is enhanced by having particles remember multiple previous successes. The additional positions introduce diversity which aids exploration. To prevent this additional diversity from hindering convergence a small memory is used and Roulette Wheel Selection is used to select a single position from memory to use when calculating particles velocities. The research shows that PSO EMP performs better than the Standard PSO in most cases and does not perform significantly worse in any case.The work presented in this thesis adds a new empirical study to the body of PSO research. It is hoped that the research will inspire new ideas and research opportunities in PSO that lead to improved PSO performance and new applications.