In this article, a number of well-established population-based optimization methods i.e. genetic algorithms, simulated annealing and population-based incremental learning are briefly reviewed and compared in terms of their philosophical basis. The use of the optimization methods for topology optimization is demonstrated. The article also presents an efficient numerical technique to prevent checkerboard formation in topology design. A number of design test-cases are assigned to measure the performances of the population-based methods. The optimum solutions obtained using these methods are illustrated and compared. Advantages and disadvantages of the optimization methods are discussed. It is shown that, based upon the convergence rate and consistency measure, simulated annealing is the best technique for this type of structural design.
The work in this paper proposes the hybridisation of the well-established strength Pareto evolutionary algorithm (SPEA2) and some commonly used surrogate models. The surrogate models are introduced to an evolutionary optimisation process to enhance the performance of the optimiser when solving design problems with expensive function evaluation. Several surrogate models including quadratic function, radial basis function, neural network, and Kriging models are employed in combination with SPEA2 using real codes. The various hybrid optimisation strategies are implemented on eight simultaneous shape and sizing design problems of structures taking into account of structural weight, lateral bucking, natural frequency, and stress. Structural analysis is carried out by using a finite element procedure. The optimum results obtained are compared and discussed. The performance assessment is based on the hypervolume indicator. The performance of the surrogate models for estimating design constraints is investigated. It has been found that, by using a quadratic function surrogate model, the optimiser searching performance is greatly improved.
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