Using the design of bicycle frames as a case study, this paper explores the potential of a multiobjective extension of the shape annealing approach to conceptual design. The key elements of this approach are a randomized search based optimization method (to simulate creativity), a generative structural shape grammar (to allow different configurations to be explored), and a multiobjective optimization approach (to identify competing concepts occupying different parts of the trade-off surface). The results presented demonstrate the success of this approach in exploring a multiplicity of different design configurations and presenting the designer with a variety of Pareto-optimal concepts worthy of further consideration.

This paper explores the potential for using optimisation methods in the conceptual design of frame structures. The key elements of our approach are a randomised search based optimisation method (to simulate creativity), a generative structural shape grammar (to allow different configurations to be explored), and a multiobjective optimisation approach (to identify competing concepts occupying different parts of the trade-off surface). The results presented for a modified version of a classic structural optimisation problem demonstrate the success of this approach in exploring a multiplicity of different design configurations and presenting the designer with a variety of Pareto-optimal concepts worthy of further consideration.

This paper describes a novel implementation of the Simulated Annealing algorithm designed to explore the trade-off between multiple objectives in optimization problems. During search, the algorithm maintains and updates an archive of non-dominated solutions between each of the competing objectives. At the end of search, the final archive corresponds to a number of optimal solutions from which the designer may choose a particular configuration. A new acceptance probability formulation based on an annealing schedule with multiple temperatures (one for each objective) is proposed along with a novel restart strategy. The performance of the algorithm is demonstrated on three examples. It is concluded that the proposed algorithm offers an effective and easily implemented method for exploring the trade-off in multiobjective optimization problems.

The paper describes a novel implementation of the Simulated Annealing algorithm designed to explore the trade-off surface in multiobjective optimisation problems, in which an appropriate annealing temperature is determined and controlled for each individual objective under consideration. The algorithm maintains and updates an archive record of the non-dominated solutions encountered during search. Thus, the final archive represents the trade-off surface between the objectives and enables the designer to make an informed decision when choosing the best overall solution. The algorithm's performance is illustrated by considering the multiobjective optimisation of bicycle frames subject to multiple loading conditions. The results obtained illustrate some important differences between the structural performance of men's and women's bicycles.