Novel particle swarm optimizers with hybrid, dynamic and adaptive neighborhood structures - Liang, Jing (2010)
Many real world problems can be formulated as optimization problems with various parameters to be optimized. Some problems only have one objective to be optimized, some may have multiple objectives to be optimized at the same time and some need to be optimized subjecting to one or more constraints. Thus numerous optimization algorithms have been proposed to solve these problems. Particle Swarm Optimizer (PSO) is a relatively new optimization algorithm which has shown its strength in the optimization world. This thesis presents two PSO variants, Comprehensive Learning PSO and Dynamic Multi-Swarm PSO, which have good global search ability and can solve complex multi-modal problems for single objective optimization. The latter one' is extended to solve constrained optimization and multi-objective optimization problems successfully with a novel constraint-handling mechanism and a novel updating criterion respectively. Subsequently, the Dynamic Multi-Swarm PSO is applied to determine the Bragg wavelengths of the sensors in an FBG sensor network and a tree search structure is designed to improve the accuracy and reduce the computation cost.