A comparison of metaheuristic multiobjective reliability-based design optimization

Abstract Multiobjective reliability-based design optimization (MORBDO) is a research area that has not been investigated in the literature compared to single-objective reliability-based design optimization. This work conducts an exhaustive study of fifteen new and popular metaheuristic MORBDO algorithms, including non-dominated sorting genetic algorithm II, differential evolution for multiobjective optimization, multiobjective evolutionary algorithm based on decomposition, multiobjective particle swarm optimization, multiobjective flower pollination algorithm, multiobjective bat algorithm, multiobjective grey wolf optimizer, multiobjective multi-verse optimization, multiobjective water cycle algorithm, success history-based adaptive multiobjective differential evolution, success history-based adaptive multiobjective differential evolution with whale optimisation, multiobjective salp swarm algorithm, real-code population-based incremental learning and differential evolution, unrestricted population size evolutionary multiobjective optimisation algorithm, and multiobjective jellyfish search optimizer. In addition, the adaptive chaos control method is employed for the above-mentioned algorithms to estimate the probabilistic constraints effectively. This comparative analysis reveals the critical technologies and enormous challenges in the reliability-based design optimization field. It also offers new insight into simultaneously dealing with the multiple conflicting design objectives and probabilistic constraints. Also, this study presents the advantage and future development trends or incurs the increased challenge of researchers to put forward an effective MORBDO algorithm that assists the complex engineering system design.