Visual mapping of multi-objective optimization problems and evolutionary algorithms
Visual mapping of multi-objective optimization problems and evolutionary algorithms
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DOI：
10.1145/3377929.3398104
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发表时间：
2020-07
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影响因子：
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通讯作者：
Kohei Yamamoto;Tomoaki Takagi;K. Takadama;Hiroyuki Sato
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文献类型：
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作者：
Kohei Yamamoto;Tomoaki Takagi;K. Takadama;Hiroyuki Sato
本文提出了一种将优化问题和进化搜索算法分别映射到二维空间的方法。在进化优化的研究领域中，基准优化问题和搜索算法一直是协同进化的。各种各样的基准问题对于搜索算法的研究是必不可少的。然而，很难定量和直观地表示基准问题之间的差异。此外，在描述搜索算法之间的差异方面也有同样的困难。所提出的方法映射优化问题的基础上，在每个优化问题的搜索算法排名的差异。此外，所提出的方法映射搜索算法的基础上，在每个搜索算法的问题排名的差异。在实验中，我们使用了ZDT，DTLZ和WFG基准套件中的26种多目标优化问题和29种进化搜索算法。其结果是，我们表明，内部功能的相似性反映在优化问题地图上的每个问题的位置，和搜索算法的设计原则的相似性反映在搜索算法地图上的每个搜索算法的位置。
This work proposes a method to map optimization problems and evolutionary search algorithms into a two-dimensional space, respectively. In the research domain of evolutionary optimization, benchmark optimization problems and search algorithms have been evolved cooperatively so far. A variety of benchmark problems are essential for the research of search algorithms. However, there is difficulty to quantitatively and visually represent differences among benchmark problems. Also, there is the same difficulty in describing differences among search algorithms. The proposed method maps optimization problems based on the difference in the search algorithm ranking on each optimization problem. Also, the proposed method maps search algorithms based on the difference in the problem ranking on each search algorithm. In the experiment, we use 26 kinds of multi-objective optimization problems included in the ZDT, DTLZ, and WFG benchmark suites and 29 kinds of evolutionary search algorithms. As a result, we show that similarities of the internal functions are reflected in the position of each problem on the optimization problem map, and the similarities of the design principles of search algorithms are reflected in the position of each search algorithm on the search algorithm map.