Curious-II: A Multi/Many-Objective Optimization Algorithm with Subpopulations based on Multi-novelty Search
Curious-II: A Multi/Many-Objective Optimization Algorithm with Subpopulations based on Multi-novelty Search
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DOI：
10.1145/3583133.3590543
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Yuzi Jiang;Danilo Vasconcellos Vargas
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文献类型：
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作者：
Yuzi Jiang;Danilo Vasconcellos Vargas
新奇搜索有效探索适应度空间的能力正在受到关注。然而，不同的新奇指标会产生不同的搜索结果。在这里，我们表明，新奇指标是互补的，多新颖性的方法大大提高了性能。具体来说，我们提出了一个多新颖性搜索多/多目标算法（好奇II），既有欧几里得距离和预测错误的新奇指标。一方面，基于欧氏距离的新奇度量使子种群探索低密度的子空间，避免早熟收敛。另一方面，预测误差新奇度量引导子种群探索具有意想不到的目标适应度的子空间。实验表明，在多新颖性算法中使用这两种新奇度量具有很强的优势。好奇II进行了比较，两个国家的最先进的算法和两个新奇的搜索为基础的算法上的WFG 1- 8测试问题，多达10个目标。它在HV指数的32项任务中的28项和IGD指数的32项任务中的27项中优于所有其他任务。
Novelty search's ability to efficiently explore the fitness space is gaining attention. Different novelty metrics, however, produce different search results. Here we show that novelty metrics are complementary and a multi-novelty approach improves the performance substantially. Specifically, we propose a multi-novelty search multi/many-objective algorithm (Curious II) that has both Euclidian distance and prediction-error novelty metrics. On the one hand, the Euclidian distance based novelty metric makes the subpopulation explore subspaces with low crowd density and avoids premature convergence. On the other hand, the prediction-error novelty metric guides a subpopulation to explore subspaces with unexpected objective fitness. Experiments reveal that using both novelty metrics in a multi-novelty algorithm has strong benefits. Curious II was compared with two state-of-the-art algorithms and two novelty search-based algorithms on the WFG 1--8 test problem with up to 10 objectives. It outperforms all the others in 28 out of 32 tasks for the HV index and in 27 out of 32 tasks for the IGD index.