| Particle Swarm Optimization (PSO) is a population-based, self-adaptive search optimization technique that has been applied to find optimal or near-optimal solutions for real-world optimization problems. In this thesis, two response methods to dynamic changes based on evolutionary computation is proposed for the particle swarm optimizer. The first method applies rank-based selection to replace half of the lower fitness population with the higher fitness population of the swarm. The second method applies mutation along with rank-based selection to improve the diversity of the population. Time-varying values are used for the acceleration coefficients with both methods to keep a higher degree of global search and a lower degree of local search at the beginning stages of the search. Performance is compared with two previous response methods using the parabolic De Jong benchmark function. |
