Dynamic changes are an important and inescapable aspect of many real-world optimization problems. Designing algorithms to find and track desirable solutions while facing challenges of dynamic optimization problems is an active research topic in the field of swarm and evolutionary computation. To evaluate and compare the performance of algorithms, it is imperative to use a suitable benchmark that generates problem instances with different controllable characteristics. In this article, we give a comprehensive review of existing benchmarks and investigate their shortcomings in capturing different problem features. We then propose a highly configurable benchmark suite, the generalized moving peaks benchmark, capable of generating problem instances whose components have a variety of properties, such as different levels of ill-conditioning, variable interactions, shape, and complexity. Moreover, components generated by the proposed benchmark can be highly dynamic with respect to the gradients, heights, optimum locations, condition numbers, shapes, complexities, and variable interactions. Finally, several well-known optimizers and dynamic optimization algorithms are chosen to solve generated problems by the proposed benchmark. The experimental results show the poor performance of the existing methods in facing new challenges posed by the addition of new properties.

中文翻译：

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连续动态优化基准测试：调查和通用测试套件


动态变化是许多现实世界优化问题的一个重要且不可避免的方面。设计算法来寻找和跟踪理想的解决方案，同时面临动态优化问题的挑战，是群体和进化计算领域的一个活跃的研究课题。为了评估和比较算法的性能，必须使用合适的基准来生成具有不同可控特征的问题实例。在本文中，我们对现有基准进行了全面回顾，并研究了它们在捕获不同问题特征方面的缺点。然后，我们提出了一个高度可配置的基准套件，即广义移动峰值基准，能够生成问题实例，其组件具有各种属性，例如不同级别的病态、变量交互、形状和复杂性。此外，所提出的基准生成的组件在梯度、高度、最佳位置、条件数、形状、复杂性和变量交互方面可以是高度动态的。最后，选择几种著名的优化器和动态优化算法来解决所提出的基准所产生的问题。实验结果表明，现有方法在面对新特性带来的新挑战时表现不佳。