To handle the multiobjective optimization problems of truss-bar design, this paper introduces a new metaheuristic multiobjective optimization algorithm. The proposed algorithm is based on a recently reported single objective version of the gradient-based optimizer (GBO) inspired by the gradients of Newton’s equations. The proposed algorithm is called as multiobjective gradient-based optimizer (MOGBO), in which two operators, such as local escaping operator and gradient search rule, and few vector sets are utilized in the search phase and the elitist non-dominated sorting mechanism is used for agent sorting to find Pareto optimal solutions. The proposed MOGBO is a posteriori method, and the traditional crowding distance mechanism is employed to confirm the coverage of the best solutions for the objectives of the given problem. The performance of the proposed MOGBO algorithm is verified and validated on different test cases, including 15 unconstraint benchmark test suites and eight constraint multiobjective truss bar design problems. To prove the superiority of the MOGBO algorithm, the performance is compared with state-of-the-art algorithms, such as multiobjective ant lion optimization (MOALO), multiobjective water cycle algorithm (MOWCA), multiobjective colliding bodies optimization (MOCBO), and non-dominated sorting gray wolf optimizer (NSGWO) in terms of metrics, such as hyper-volume, coverage, inverted generational distance, pure diversity, Spacing, Spread, coverage Pareto front, diversity maintenance, generational distance, and runtime. The solutions obtained by the proposed MOGBO algorithm is highly accurate and requires less runtime than the other selected algorithms. The obtained results also show the efficiency of the MOGBO in solving most of all the complex multiobjective problems. This research will be further backed up with external guidance for the future research at https://premkumarmanoharan.wixsite.com/mysite.

为了解决桁架设计的多目标优化问题，本文介绍了一种新的元启发式多目标优化算法。拟议的算法基于牛顿方程式梯度启发的最近报告的基于梯度的优化器（GBO）的单一目标版本。该算法被称为多目标基于梯度的优化器（MOGBO），其中两个算子（例如局部转义算子和梯度搜索规则）在搜索阶段使用的向量集很少，并且使用了精英非支配排序机制进行代理商分类以找到帕累托最佳解决方案。提出的MOGBO是一种后验方法，并且采用传统的拥挤距离机制来确认针对给定问题的目标的最佳解决方案的覆盖范围。所提出的MOGBO算法的性能在不同的测试案例中得到了验证和验证，包括15个无约束基准测试套件和8个约束多目标桁架设计问题。为了证明MOGBO算法的优越性，将性能与最新的算法进行了比较，例如多目标蚁群优化（MOALO），多目标水循环算法（MOWCA），多目标碰撞体优化（MOCBO）和非主导的排序灰狼优化器（NSGWO）的指标，例如超容量，覆盖率，反向世代距离，纯多样性，间距，传播，帕累托阵线，多样性维护，世代距离和运行时间。通过提出的MOGBO算法获得的解决方案非常准确，并且比其他选定算法所需的运行时间更少。获得的结果还显示了MOGBO在解决所有复杂的多目标问题中的效率。该研究将通过https://premkumarmanoharan.wixsite.com/mysite上的未来研究的外部指导得到进一步支持。