The challenges of the most multi-objective particle swarm optimization (MOPSO) algorithms are to improve the selection pressure, equilibrate the convergence and diversity when tackling large-scale many-objective problems. To overcome these challenges, this paper proposes a novel PSO-based large-scale many-objective algorithm, named as LMPSO. In LMPSO, the Alpha-stable mutation is performed to enhance the diversity of swarm for avoiding premature convergence. And the parameters of PSO and Alpha-stable mutation are dynamically set following the Logistic function, which emphasize different convergence and diversity at different optimization stages. Moreover, LMPSO adopts a fitness to maintain the external archive, and the calculation of fitness is based on binary additive epsilon indicator. The binary indicator is also used to update the personal best of particles to avoid wrongly selecting dominance resistance solutions (DRSs). Aims for improving the selection pressure, the proposed algorithm employ a concept of dominance resistance error to identify the DRSs. To verify this idea, the DTLZ, ZDT, and LSMOP test suites with up to 1000 decision variables and 10-objective are used to qualify the performance of LMPSO. The simulations reveal the fact that the LMPSO significantly outruns the several chosen state-of-the-art algorithms when solving large-scale many-objective test instances.

大多数多目标粒子群优化（MOPSO）算法的挑战是在解决大规模多目标问题时提高选择压力，平衡收敛性和多样性。为了克服这些挑战，本文提出了一种新颖的基于PSO的大规模多目标算法，称为LMPSO。在LMPSO中，执行Alpha稳定突变以增强群的多样性，以避免过早收敛。根据Logistic函数动态设置PSO和Alpha稳定突变的参数，强调在不同优化阶段的不同收敛性和多样性。此外，LMPSO采用适合度来维护外部存档，适合度的计算基于二进制加性ε指标。二进制指示器还用于更新粒子的最佳性能，以避免错误选择主导抗性解决方案（DRS）。为了提高选择压力，该算法采用了优势电阻误差以识别DRS。为了验证这一想法，使用具有多达1000个决策变量和10个目标的DTLZ，ZDT和LSMOP测试套件来验证LMPSO的性能。仿真结果表明，在解决大规模多目标测试实例时，LMPSO大大超出了几种选定的最新算法。