Efficient decoupling-assisted evolutionary/metaheuristic framework for expensive reliability-based design optimization problems. (1st November 2022)

• Record Type: Journal Article
• Authors: Meng, Zeng; Rıza Yıldız, Ali; Mirjalili, Seyedali
• Is Part Of: Expert systems with applications. Volume 205(2022)
• Publication Date: 2022-11-01
• Subjects: Reliability-based design optimization -- Fractional-order equilibrium optimizer algorithm -- Metaheuristic -- Particle's memory saving strategy -- Evolutionary algorithm -- Optimization -- Algorithm; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117640
• Languages: English
• ISSNs: 0957-4174

Abstract

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.