Real-world optimization problems have been comparatively difficult to solve due to the complex nature of the objective function with a substantial number of constraints. To deal with such problems, several metaheuristics as well as constraint handling approaches have been suggested. To validate the effectiveness and strength, performance of a newly designed approach should be benchmarked by using some complex real-world problems, instead of only the toy problems with synthetic objective functions, mostly arising from the area of numerical analysis. A list of standard real-life problems appears to be the need of the time for benchmarking new algorithms in an efficient and unbiased manner. In this study, a set of 57 real-world Constrained Optimization Problems (COPs) are described and presented as a benchmark suite to validate the COPs. These problems are shown to capture a wide range of difficulties and challenges that arise from the real life optimization scenarios. Three state-of-the-art constrained optimization methods are exhaustively tested on these problems to analyze their hardness. The experimental outcomes reveal that the selected problems are indeed challenging to these algorithms, which have been shown to solve many synthetic benchmark problems easily.

现实世界中的优化问题由于目标函数的复杂性和大量约束而难以解决。为了解决这些问题，已经提出了几种元启发法以及约束处理方法。为了验证有效性和强度，应该使用一些复杂的实际问题对新设计方法的性能进行基准测试，而不仅仅是具有综合目标函数的玩具问题，这些问题主要来自于数值分析领域。标准的现实生活问题列表似乎是需要时间以有效且公正的方式对新算法进行基准测试的问题。在这项研究中，描述了一组57个现实世界中的约束优化问题（COP），并将其作为验证COP的基准套件。这些问题显示出可以解决现实生活中最优化方案带来的各种困难和挑战。针对这些问题，对三种最先进的约束优化方法进行了详尽的测试，以分析其硬度。实验结果表明，选择的问题确实对这些算法具有挑战性，这些算法已经证明可以轻松解决许多综合基准问题。