在十几年前,元启发式优化算法引起了广泛的注意。这些算法中不同参数的调整是个耗时很长的过程,常需通过反复计算进行。故引入集中因子,评价这些算法的性能。集中因子可对参数的匹配性进行估计,并能增强算法自动调整参数的能力。分别采用遗传算法、蚁群优化算法,粒子群优化算法和大爆炸-大危机算法,对某钢支撑框架进行拓扑优化。数值结果表明,这些算法在解决优化问题时具有一些共同点。 A dozen years ago, meta-heuristic optimization algorithms attracted a lot of attention. The adjustment of different parameters in these algorithms is a long and time consuming process and often needs to be performed repeatedly. Therefore, the introduction of concentration factor, evaluation of the performance of these algorithms. The concentration factor estimates the match of parameters and enhances the algorithm’s ability to automatically adjust parameters. The genetic algorithm, ant colony optimization algorithm, particle swarm optimization algorithm and big-bang-big crisis algorithm are respectively used to optimize the topology of a steel support frame. Numerical results show that these algorithms have some commonalities in solving optimization problems.