The constant development of new metaheuristic algorithms has led to a saturation in the field of stochastic search. There are now hundreds of different algorithms that can be used to solve any problem. To produce a good performance, every metaheuristic method needs to address a satisfactory equilibrium between exploration and exploitation of the search space. Although exploration and exploitation represent two fundamental concepts in metaheuristics, the main questions about their combination and balance have not been yet completely understood. Most of the existent analyzes conducted on metaheuristic techniques consider only the comparison of their final results which cannot evaluate the nature of a good or bad balance. This paper presents an experimental analysis that quantitatively evaluates the balance between exploration and exploitation of several of the most important and better-known metaheuristic algorithms. In the study, a dimension-wise diversity measurement is used to assess the balance of each scheme considering a representative set of 42 benchmark problems that involve multimodal, unimodal, composite and shifted functions. As a result, the analysis provides several observations that allow understanding how this balance affects the results in each type of functions, and which balance is producing better solutions.

新的元启发式算法的不断发展导致了随机搜索领域的饱和。现在有数百种不同的算法可用于解决任何问题。为了产生良好的性能，每种元启发式方法都需要解决探索空间与探索空间之间令人满意的平衡问题。尽管探索和开发是元启发法中的两个基本概念，但是关于它们的组合和平衡的主要问题尚未完全理解。现有的大多数关于元启发式技术的分析都只考虑了其最终结果的比较，而这些结果无法评估好坏平衡的性质。本文提出了一项实验分析，定量评估了几种最重要和最著名的元启发式算法在探索和开发之间的平衡。在研究中，考虑了涉及多峰，单峰，复合和移位函数的42个基准问题的代表性集合，使用了按维度进行分集的度量来评估每个方案的平衡。结果，该分析提供了一些观察结果，这些观察结果使您可以了解这种平衡如何影响每种功能类型的结果，以及哪种平衡可以产生更好的解决方案。考虑到42个涉及多峰，单峰，复合和移位函数的代表性问题的代表性集合，使用了基于维度的多样性度量来评估每个方案的平衡。结果，该分析提供了多个观察结果，这些观察结果使您可以了解这种平衡如何影响每种功能类型的结果，以及哪种平衡可以产生更好的解决方案。考虑到42个涉及多峰，单峰，复合和移位函数的代表性问题的代表性集合，使用了基于维度的多样性度量来评估每个方案的平衡。结果，该分析提供了一些观察结果，这些观察结果使您可以了解这种平衡如何影响每种功能类型的结果，以及哪种平衡可以产生更好的解决方案。