Multi-objective shape control of prestressed structures with genetic algorithms

Abstract

The shape of a prestressed structure is sensitive to external errors/stimuli due to its lightweight and high flexibility. When the shape distortion is deemed unacceptable, adjustment of the nodal positions is required. In this article, the shape control of a prestressed structure is modelled as a multi-objective optimization problem. A novel multi-objective genetic algorithm has been proposed to solve this problem. In the proposed algorithm, a niche technique consisting of a crowding check and a filter process is introduced to ensure the diversity of the population, and simultaneously, to accelerate the revolution process. On the basis of a ranking scheme of the individuals’ degree of constraint violation, a problem-independent penalty method for constraint handling has been proposed. In addition, some well-developed techniques, such as fitness evaluation with a ranking procedure and termination judging with the distance to the idea point, are properly selected and used. The proposed algorithm performs well in seeking Pareto optimal sets for the shape control of prestressed structures, which is verified by two examples.