Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine metaheuristic algorithms, namely, salp swarm algorithm (SSA), multi-verse optimizer (MVO), moth-flame optimizer (MFO), atom search optimization (ASO), ecogeography-based optimization (EBO), queuing search algorithm (QSA), equilibrium optimizer (EO), evolutionary strategy (ES) and hybrid self-adaptive orthogonal genetic algorithm (HSOGA). The efficiency of these algorithms is evaluated on eight mechanical design problems using the solution quality and convergence analysis, which verifies the wide applicability of these algorithms to real-world application problems.

在使用新开发的高效群智能算法时，确定实际机械设计问题的解决方案是一项具有挑战性的任务。有很多困难需要解决，包括但不限于混合决策变量、不同约束、固有错误、冲突目标和众多局部最优解。这项工作分析了九种元启发式算法的行为，即salp swarm算法（SSA）、多节优化器（MVO）、飞蛾火焰优化器（MFO）、原子搜索优化（ASO）、基于生态地理学的优化（EBO）、排队搜索算法（QSA）、均衡优化器（EO）、进化策略（ES）和混合自适应正交遗传算法（HSOGA）。