Multi-objective truss optimisation is a research topic that has been less investigated in the literature compared to the single-objective cases. This paper investigates the comparative performance of fourteen new and established multi-objective metaheuristics when solving truss optimisation problems. The optimisers include multi-objective ant lion optimiser, multi-objective dragonfly algorithm, multi-objective grasshopper optimisation algorithm, multi-objective grey wolf optimiser, multi-objective multi-verse optimisation, multi-objective water cycle algorithm, multi-objective Salp swarm algorithm, success history-based adaptive multi-objective differential evolution, success history–based adaptive multi-objective differential evolution with whale optimisation, non-dominated sorting genetic algorithm II, hybridisation of real-code population-based incremental learning and differential evolution, differential evolution for multi-objective optimisation, multi-objective evolutionary algorithm based on decomposition, and unrestricted population size evolutionary multi-objective optimisation algorithm. The design problem is assigned to minimise structural mass and compliance subject to stress constraints. Eight classical trusses found in the literature are used for setting up the design test problems. Various optimisers are then implemented to tackle the problems. A comprehensive comparative study is given to critically analyse the performance of all algorithms in this problem area. The results provide new insights to the pros and cons of evolutionary multi-objective optimisation algorithms when addressing multiple, often conflicting objective in truss optimisation. The results and findings of this work assist with not only solving truss optimisation problem better but also designing customised algorithms for such problems.

多目标桁架优化是一个研究主题，与单目标案例相比，文献研究较少。本文研究了十四种新的和建立的多目标元启发式方法在解决桁架优化问题时的比较性能。优化器包括多目标蚁狮优化器，多目标蜻蜓算法，多目标蚱hopper优化算法，多目标灰狼优化器，多目标多逆优化，多目标水循环算法，多目标萨尔普蜂群算法，基于成功历史的自适应多目标差分进化，鲸鱼优化的基于自适应成功的多目标差分进化，非支配排序遗传算法II，基于实码的基于人口的增量学习与差分进化的混合，用于多目标优化的差分进化，基于分解的多目标进化算法以及人口规模不受限制的进化多目标优化算法。分配设计问题是为了使结构质量和受应力约束的柔性最小。文献中发现的八个经典桁架用于设置设计测试问题。然后实施各种优化器以解决这些问题。进行了全面的比较研究，以严格分析此问题区域中所有算法的性能。这些结果为解决多目标优化的进化多目标优化算法的优缺点提供了新的见解。桁架优化中经常有冲突的目标。这项工作的结果和发现不仅有助于更好地解决桁架优化问题，而且有助于设计针对此类问题的定制算法。