Non-traditional optimization tools have proved their potential in solving various types of optimization problems. These problems deal with either single objective or multiple/many objectives. Bonobo Optimizer (BO) is an intelligent and adaptive metaheuristic optimization algorithm inspired from the social behavior and reproductive strategies of bonobos. There is no study in the literature to extend this BO to solve multi-objective optimization problems. This paper presents a Multi-objective Bonobo Optimizer (MOBO) to solve different optimization problems. Three different versions of MOBO are proposed in this paper, each using a different method, such as non-dominated sorting with adaptation of grid approach; a ranking scheme for sorting of population with crowding distance approach; decomposition technique, wherein the solutions are obtained by dividing a multi-objective problem into a number of single-objective problems. The performances of all three different versions of the proposed MOBO had been tested on a set of thirty diversified benchmark test functions, and the results were compared with that of four other well-known multi-objective optimization techniques available in the literature. The obtained results showed that the first two versions of the proposed algorithms either outperformed or performed competitively in terms of convergence and diversity compared to the others. However, the third version of the proposed techniques was found to have the poor performance.

非传统的优化工具已经证明了其解决各种类型的优化问题的潜力。这些问题涉及单个目标或多个/许多目标。no黑猩猩优化程序（BO）是一种智能的自适应元启发式优化算法，其灵感来自于bo黑猩猩的社交行为和生殖策略。文献中没有研究将该BO扩展为解决多目标优化问题。本文提出了一种多目标Bonobo优化器（MOBO），以解决不同的优化问题。本文提出了三种不同的MOBO版本，每种版本都使用不同的方法，例如采用网格方法的非主导排序。一种采用拥挤距离方法对人口进行排序的排序方案；分解技术 其中，通过将多目标问题划分为多个单目标问题来获得解。建议的MOBO的所有三个不同版本的性能已在一组三十个多样化的基准测试功能上进行了测试，并将结果与​​文献中提供的其他四个著名的多目标优化技术进行了比较。获得的结果表明，与其他算法相比，所提出算法的前两个版本在收敛性和多样性方面均优于或具有竞争性。但是，发现所建议技术的第三版性能较差。拟议的MOBO的所有三个不同版本的性能已在一组三十个多样化的基准测试功能上进行了测试，并将结果与​​文献中提供的其他四个著名的多目标优化技术进行了比较。获得的结果表明，与其他算法相比，所提出算法的前两个版本在收敛性和多样性方面均优于或具有竞争性。但是，发现所建议技术的第三版性能较差。拟议的MOBO的所有三个不同版本的性能已在一组三十个多样化的基准测试功能上进行了测试，并将结果与​​文献中提供的其他四个著名的多目标优化技术进行了比较。获得的结果表明，与其他算法相比，所提出算法的前两个版本在收敛性和多样性方面均优于或具有竞争性。但是，发现所建议技术的第三版性能较差。获得的结果表明，与其他算法相比，所提出算法的前两个版本在收敛性和多样性方面均优于或具有竞争性。但是，发现所建议技术的第三版性能较差。获得的结果表明，与其他算法相比，所提出算法的前两个版本在收敛性和多样性方面均优于或具有竞争性。但是，发现所建议技术的第三版性能较差。