The gravitational search algorithm (GSA) is a meta-heuristic algorithm based on the theory of Newtonian gravity. This algorithm uses the gravitational forces among individuals to move their positions in order to find a solution to optimization problems. Many studies indicate that the GSA is an effective algorithm, but in some cases, it still suffers from low search performance and premature convergence. To alleviate these issues of the GSA, an aggregative learning GSA called the ALGSA is proposed with a self-adaptive gravitational constant in which each individual possesses its own gravitational constant to improve the search performance. The proposed algorithm is compared with some existing variants of the GSA on the IEEE CEC2017 benchmark test functions to validate its search performance. Moreover, the ALGSA is also tested on neural network optimization to further verify its effectiveness. Finally, the time complexity of the ALGSA is analyzed to clarify its search performance.

重力搜索算法（GSA）是基于牛顿重力理论的元启发式算法。该算法利用个体之间的重力来移动其位置，以找到优化问题的解决方案。许多研究表明，GSA是一种有效的算法，但是在某些情况下，它仍然遭受搜索性能低下和收敛过早的困扰。为了缓解GSA的这些问题，提出了一种称为ALGSA的集合学习型GSA，它具有自适应重力常数，其中每个人都有自己的重力常数以提高搜索性能。将该算法与IEEE CEC2017基准测试功能上GSA的一些现有变体进行了比较，以验证其搜索性能。此外，ALGSA还经过了神经网络优化测试，以进一步验证其有效性。最后，分析了ALGSA的时间复杂度，以阐明其搜索性能。