Avoidance Techniques Neighbourhood Topologies in Particle Swarm Optimisation

Particle swarm optimisation (PSO) is both a heuristic and stochastic optimisation algorithm. The purpose of these algorithms is to give approximate solutions to problems which would be otherwise too difficult to solve. The PSO algorithm optimises the problem space as a result of particles converging on the best known solution after a period of exploration. This thesis will introduce a PSO variant with Avoidance of Worst Locations (AWL). The motion of the particles in PSO AWL will be different from that of the standard PSO as a result of their ability to remember their worst locations. The particles will use this new information to improve their search of the problem space by spending less time in the worst positions of the problem space. It is found that a subtle influence from the worst location results in the optimum performance. The proposed PSO AWL has a superior performance when compared to the standard PSO and also previous implementations of worst locations. This thesis will also examine the effect of alternative neighbourhood topologies on the performance of each PSO. It is observed that the dynamic topology, which has be dubbed Gradually Increasing Directed Neighbourhoods (GIDN), further augments the performance of PSO AWL. Each of these PSO variants are then applied to the Dynamic Economic Emissions Dispatch (DEED) problem to compare their effectiveness on constrained multi objective problems. The PSO AWL performed significantly better than the standard PSO on the DEED problem with each topology. The application of this research to the DEED model demonstrates the impact of these alternative PSO approaches to real world problem domains.