The q-gradient is the generalization of the gradient based on the q-derivative. The q-version of the steepest descent method for unconstrained multiobjective optimization problems is constructed and recovered to the classical one as q equals 1. In this method, the search process moves step by step from global at the beginning to particularly neighborhood at last. This method does not depend upon a starting point. The proposed algorithm for finding critical points is verified in the numerical examples.

中文翻译：

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在q无约束多目标优化问题-steepest下降法

的q -gradient是基于所述梯度的泛化q -衍生物。构造了用于无约束多目标优化问题的最速下降法的q版本，并在q等于1的情况下恢复为经典方法。在这种方法中，搜索过程逐步从最初的整体移动到最后的特定邻域。该方法不依赖于起点。数值算例验证了所提出的关键点算法。