To solve constrained optimization problems (COPs), it is crucial to guide the infeasible solution to a feasible region. Gradient-based repair (GR) is a successful repair strategy, where the forward difference is often used to estimate the gradient. However, GR has major deficiencies. First, it is difficult to deal with individuals falling into the local optima. Second, large amounts of fitness evaluations are required to estimate the gradient. In this paper, we proposed a new repair strategy, random direction repair (RDR). RDR generates a set of random directions, and calculates the repair direction and the repair step size of infeasible individual to reduce its constraint violation. Since the introduction of randomness, RDR could deal with individuals falling into the local optima. Furthermore, RDR only requires a few number of fitness evaluation. To demonstrate the performance of RDR, RDR was embedded into two state-of-the-art evolutionary continuous constrained optimization algorithms, tested on the Congress on Evolutionary Computation 2017 constrained real-parameter optimization benchmark. Experimental results demonstrated that RDR combined with evolutionary algorithms are highly competitive.

要解决约束优化问题（COP），将不可行的解决方案引导至可行区域至关重要。基于梯度的修复（GR）是一种成功的修复策略，其中前向差异通常用于估计梯度。但是，遗传资源存在重大缺陷。首先，很难应对陷入局部最优状态的个人。其次，需要大量的适合度评估才能估算出梯度。在本文中，我们提出了一种新的修复策略，即随机方向修复（RDR）。RDR生成一组随机方向，并计算不可行个体的修复方向和修复步长，以减少其约束冲突。自引入随机性以来，RDR可以处理陷入局部最优状态的个人。此外，RDR仅需要进行一些适应性评估。为了演示RDR的性能，将RDR嵌入了两个最新的演化连续约束优化算法中，并在Congress on Evolution Computation 2017约束实参数优化基准上进行了测试。实验结果表明，RDR与进化算法相结合具有很高的竞争力。