Modified Particle Swarm Optimizers and their Application to Robust Design and Structural Optimization
Bin Yang · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · 2009
2. Univ.-Prof. Dr.-Ing. G. Müller Die Dissertation wurde am 07.04.2009 bei der Technischen Universität München eingereicht und durch die Fakultät für Bauingenieur – und Vermessungswesen am Many scientific, engineering and economic problems involve optimization. In reaction to that, numerous optimization algorithms have been proposed. Particle Swarm Optimization (PSO) is a new paradigm of Swarm Intelligence which is inspired by concepts from ’Social Psychology ’ and ’Artificial Life’. Essentially, PSO proposes that the co-operation of individuals promotes the evolution of the swarm. In terms of optimization, the hope would be to enhance the swarm’s ability to search on a global scale so as to determine the global optimum in a fitness landscape. It has been empirically shown to perform well with regard to many different kinds of optimization problems. PSO is particularly a preferable candidate to solve highly nonlinear, non-convex and even discontinuous problems. The main ambition of this thesis is to propose two enhanced versions of PSO (Modified Guaranteed Convergence PSO (MGCPSO) and Modified Lbest based PSO (LPSO)) and to extend them to