A wide range of process systems engineering problems involve an optimisation formulation that is difficult to solve due to sources of discontinuity and non-convexity and a high number of constraints to satisfy. Differential Evolution algorithm (DE) has proven to be robust for the solution of highly non-convex and mixed-integer problems; nevertheless, its performance greatly depends on the constraint-handling technique used. In this study, numerical comparisons of some state-of-the-art constraint-handling techniques are performed: static penalty function, stochastic ranking, feasibility rules, $\epsilon$ constrained method and gradient-based repair. The obtained results show that the gradient-based repair technique deserves a special attention when solving highly constrained problems. This technique enables to efficiently satisfy both inequality and equality constraints, which makes it particularly adapted for the solution of process engineering optimization problems.

大量的过程系统工程问题涉及优化公式，由于不连续和不凸的来源以及需要满足的大量约束条件，因此很难解决该优化公式。事实证明，差分进化算法（DE）对于解决高度非凸和混合整数问题具有很强的鲁棒性。但是，其性能在很大程度上取决于所使用的约束处理技术。在这项研究中，我们对一些最先进的约束处理技术进行了数值比较：静态惩罚函数，随机排名，可行性规则，$\epsilon$约束方法和基于梯度的修复。获得的结果表明，在解决高度受限的问题时，基于梯度的修复技术值得特别注意。该技术能够有效地满足不等式和相等性约束，这使其特别适合于解决工艺工程优化问题。