Abstract: Considering the challenges of short product life cycles
and growing variant diversity, cost minimization and manufacturing
flexibility increasingly gain importance to maintain a competitive
edge in today’s global and dynamic markets. In this context, an
aerodynamic part feeding system for high-speed industrial assembly
applications has been developed at the Institute of Production
Systems and Logistics (IFA), Leibniz Universitaet Hannover. The
aerodynamic part feeding system outperforms conventional systems
with respect to its process safety, reliability, and operating speed. In
this paper, a multi-objective optimisation of the aerodynamic feeding
system regarding the orientation rate, the feeding velocity, and the
required nozzle pressure is presented.
Abstract: Many real-world optimization problems involve multiple conflicting objectives and the use of evolutionary algorithms to solve the problems has attracted much attention recently. This paper investigates the application of multi-objective optimization technique for the design of a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the performance of a power system. The design objective is to improve both rotor angle stability and system voltage profile. A Genetic Algorithm (GA) based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimisation problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented to show the effectiveness and robustness of the proposed approach.
Abstract: In this paper, the optimum weight and cost of a laminated composite plate is seeked, while it undergoes the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill theory is used as the failure criterion. The theory of analysis was based on the Classical Lamination Theory (CLT). A newly type of Genetic Algorithm (GA) as an optimization technique with a direct use of real variables was employed. Yet, since the optimization via GAs is a long process, and the major time is consumed through the analysis, Radial Basis Function Neural Networks (RBFNN) was employed in predicting the output from the analysis. Thus, the process of optimization will be carried out through a hybrid neuro-GA environment, and the procedure will be carried out until a predicted optimum solution is achieved.