Efficient decoupling-assisted evolutionary/metaheuristic framework for expensive reliability-based design optimization problems. (1st November 2022) - Meng, Zeng; Rıza Yıldız, Ali; Mirjalili, Seyedali
Highlights: A generalized decoupling metaheuristic RBDO framework is proposed. A particle's memory saving strategy is developed to provide effective guidance. A new adaptive fractional-order equilibrium optimizer algorithm is developed. Numerical and engineering examples illustrate the superiority of the proposed algorithm. Abstract: Reliability-based design optimization (RBDO) algorithm is to minimize the objective under the probabilistic factors. While gradient-based and classical evolutionary RBDO algorithms provide promising performance on simple optimization problems, they are likely to perform poorly on challenging problems, including the multimodal functions, discrete design spaces, non-differential problems, etc. This paper proposes a unified framework to improve the performance of existing RBDO algorithms for complex RBDO problems. Our framework is based on three new strategies: generalized decoupling evolutionary and metaheuristic RBDO framework, particle's memory saving strategy, and adaptive fractional-order equilibrium optimizer algorithm. The proposed algorithm is characterized by a decoupling strategy to enable the parallel operation of the inner reliability computation and outer deterministic optimization, a particle's memory saving strategy to provide effective guidance from the previous iteration, and the adaptive fractional-order equilibrium optimizer algorithm to enhance the search efficiency and global convergence capacity. To evaluate the performance of the proposed algorithm, a wide range of experiments are conducted on different types of use cases. The experimental results demonstrate that our algorithm provides superior performance over other comparative algorithms.