In the growing field of swarm-based metaheuristics, it is widely agreed that the behaviour of an algorithm, in terms of a good balance of exploration and exploitation, plays an important part in its success. Despite this, the influence that the characteristics of an optimisation problem may have on the behaviour of an algorithm is largely ignored. The characteristics of an optimisation problem can be intuitively understood and quantified in terms of fitness landscapes characteristics (FLCs). Similarly, the behaviour of a swarm-based algorithm can be quantified in terms of its diversity rate-of-change (DRoC). This study investigates correlations between the FLCs of optimisation problems and the DRoCs of particle swarm optimisers. The result is a collection of findings about links between particular problem characteristics and algorithm behaviour. The approach followed in this study may also be used as a template for further studies that broaden the scope of this study.

在基于群体的元启发式算法不断发展的领域中，人们普遍认为，算法的行为在探索与开发之间取得良好的平衡，在其成功中起着重要的作用。尽管如此，很大程度上忽略了优化问题的特征可能对算法行为的影响。可以根据适合度景观特征（FLC）直观地了解和量化优化问题的特征。类似地，基于群算法的行为可以根据其多样性变化率（DRoC）进行量化。这项研究调查了优化问题的FLC与粒子群优化程序的DRoC之间的相关性。结果是关于特定问题特征和算法行为之间的联系的发现的集合。