In recent days, swarm intelligent (SI) optimization algorithms have been proved to be a powerful framework for finding tradeoff solutions of multi-objective optimization problems (MOPs). Many researchers have proposed various SI optimization algorithms. Multi-objective artificial flora (MOAF) optimization algorithm is a recently proposed algorithm for solving MOPs. However, problems of decreased population diversity and uniformity of solutions distribution in the late evolutionary period is existed in the algorithm. Hence, this paper proposes a knee point-driven MOAF (kpMOAF) optimization algorithm to address the vulnerability of MOAF optimization algorithm. Knee points of the non-dominant solutions are taken by the proposed algorithm as criterion to guide the population evolution. Researchers have proved that select knee points equals to select a large hypervolume. Therefore, using it as criterion is an effective way to enhance the population convergence rate and maintain the diversity of solutions. In addition, adaptive neighborhood control method is introduced in the evolution process to improve the algorithm development capability. Simulation results on 10 benchmark functions demonstrate the competitiveness of kpMOAF optimization algorithm.

最近几天，群智能 (SI) 优化算法已被证明是寻找多目标优化问题 (MOP) 权衡解决方案的强大框架。许多研究人员提出了各种SI优化算法。多目标人工植物群 (MOAF) 优化算法是最近提出的用于解决 MOP 的算法。但该算法存在进化后期种群多样性下降和解分布均匀的问题。因此，本文提出了一种膝点驱动的MOAF（kpMOAF）优化算法来解决MOAF优化算法的脆弱性。所提出的算法将非优势解的拐点作为指导种群进化的准则。研究人员已经证明，选择拐点等于选择一个大的超体积。因此，以它为准则是提高种群收敛速度和保持解多样性的有效途径。此外，在进化过程中引入了自适应邻域控制方法，以提高算法开发能力。10 个基准函数的仿真结果证明了 kpMOAF 优化算法的竞争力。