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This article investigates a non-population-based optimization method using mathematical morphology and the radial basis function (RBF) for multimodal computationally intensive functions. To obtain several feasible solutions, mathematical morphology is employed to search promising regions. Sequential quadratic programming is used to exploit the possible areas to determine the exact positions of the potential optima. To relieve the computational burden, metamodelling techniques are employed. The RBF metamodel in different iterations varies considerably so that the positions of potential optima are moving during optimization. To find the pair of correlative potential optima between the latest two iterations, a tolerance is presented. Furthermore, to ensure that all the output minima are the global or local optima, an optimality judgement criterion is introduced. [ABSTRACT FROM AUTHOR]

Product lifetime cost is largely determined by product lifetime reliability. In product design, the former is minimized while the latter is treated as a constraint and is usually estimated by statistical means. In this work, a new lifetime cost optimization model is developed where the product lifetime reliability is predicted with computational models derived from physical principles. With the physics-based reliability method, the state of a system is indicated by computational models, and the time-dependent system reliability is then predicted for a given set of distributions and stochastic processes in the model input. A sampling approach to extreme value distributions of input stochastic processes is employed to make the system reliability analysis efficient and accurate. The physics-based reliability analysis is integrated with the lifetime cost model. The integration enables the minimal lifetime costs including those of maintenance and warranty. Two design examples are used to demonstrate the proposed model. [ABSTRACT FROM AUTHOR]

This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems. [ABSTRACT FROM AUTHOR]