Abstract
When making statistical analysis of single-objective optimization algorithms’ performance, researchers usually estimate it according to the obtained optimization results in the form of minimal/maximal values. Though this is a good indicator about the performance of the algorithm, it does not provide any information about the reasons why it happens. One possibility to get additional information about the performance of the algorithms is to study their exploration and exploitation abilities. In this paper, we present an easy-to-use step by step pipeline that can be used for performing exploration and exploitation analysis of single-objective optimization algorithms. The pipeline is based on a web-service-based e-Learning tool called DSCTool, which can be used for making statistical analysis not only with regard to the obtained solution values but also with regard to the distribution of the solutions in the search space. Its usage does not require any special statistic knowledge from the user. The gained knowledge from such analysis can be used to better understand algorithm’s performance when compared to other algorithms or while performing hyperparameter tuning.
Highlights
In-depth understanding of optimization algorithm behavior is crucial for achieving relevant progress in the optimization research field [1]
The algorithm will not be able to find the region with optimal solution and consequent exploitation is limited to the region identified by exploration
To provide more information about the exploration and exploitation abilities of the compared algorithms during the optimization process, we compared them in different time points, D*(1000, 10,000, 100,000, and 1,000,000)
Summary
In-depth understanding of optimization algorithm behavior is crucial for achieving relevant progress in the optimization research field [1]. Analyzing optimization algorithms from the perspective of achieved results (minimum/maximum value) provides only information about their final performance [2]. This could be probably enough for the algorithm that returns the best results for all optimization problems, since it outperforms every other algorithm on every problem. The exploration is related to the algorithm’s ability to efficiently explore the search space, so it can find the region that contains optimal solution quickly. While the exploitation is related to the algorithm’s ability to efficiently exploit the knowledge about the region, identified by the exploration, to find the actual optimal solution. For every algorithm, it is paramount to have a good exploration ability before one can even consider evaluating the exploitation ability
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