论文标题
确定GSA的初始重力常数的启发式
A heuristic to determine the initial gravitational constant of the GSA
论文作者
论文摘要
重力搜索算法（GSA）是一种基于牛顿的重力和动态定律的优化算法。 GSA于2009年推出，已经有几个版本和应用程序。但是，其性能取决于其参数的值，这些值是经验确定的。因此，它的一般性受到损害，因为适合特定应用程序的参数不一定适合另一个应用程序。本文提出了具有归一化重力常数（GSA-NGC）的重力搜索算法，该算法定义了一种新的启发式，以确定GSA的初始重力常数。新的启发式措施是基于Brans-Dicke引力理论，并考虑了应用程序搜索空间的多个维度。它旨在改善最终解决方案并减少GSA的迭代次数和过早融合。 GSA-NGC经过实验验证，证明适用于各种应用，并显着提高GSA的一般性，性能和效率。
The Gravitational Search Algorithm (GSA) is an optimization algorithm based on Newton's laws of gravity and dynamics. Introduced in 2009, the GSA already has several versions and applications. However, its performance depends on the values of its parameters, which are determined empirically. Hence, its generality is compromised, because the parameters that are suitable for a particular application are not necessarily suitable for another. This paper proposes the Gravitational Search Algorithm with Normalized Gravitational Constant (GSA-NGC), which defines a new heuristic to determine the initial gravitational constant of the GSA. The new heuristic is grounded in the Brans-Dicke theory of gravitation and takes into consideration the multiple dimensions of the search space of the application. It aims to improve the final solution and reduce the number of iterations and premature convergences of the GSA. The GSA-NGC is validated experimentally, proving to be suitable for various applications and improving significantly the generality, performance, and efficiency of the GSA.
