SPINEX-Optimization:基于相似性的预测与可解释邻域探索,适用于单一、多重和多目标优化

MZ Naser, Ahmed Z Naser
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引用次数: 0

摘要

本文介绍了 SPINEX(基于相似性预测与可解释邻域探索)套件的扩展,现已扩展到单目标、多目标和多目标优化问题。新开发的SPINEX-Optimization算法通过考虑各种解决方案之间的相似性,在低维度和高维度上采用了一种细致入微的优化方法。我们进行了广泛的基准测试,在55个数学基准函数和现实场景中将SPINEX-Optimization与10个单一优化算法和8个多/多优化算法进行了比较。然后,我们从低维和高维、目标数量和群体规模的可扩展性和计算效率方面评估了所提算法的性能。结果表明,SPINEX-Optimization 始终优于大多数算法,在管理复杂场景方面表现出色,尤其是在高维场景中。通过深入的实验和可视化方法,该算法在可解释性、帕累托效率和适度复杂性方面的优势得到了凸显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SPINEX-Optimization: Similarity-based Predictions with Explainable Neighbors Exploration for Single, Multiple, and Many Objectives Optimization
This article introduces an expansion within SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) suite, now extended to single, multiple, and many objective optimization problems. The newly developed SPINEX-Optimization algorithm incorporates a nuanced approach to optimization in low and high dimensions by accounting for similarity across various solutions. We conducted extensive benchmarking tests comparing SPINEX-Optimization against ten single and eight multi/many optimization algorithms over 55 mathematical benchmarking functions and realistic scenarios. Then, we evaluated the performance of the proposed algorithm in terms of scalability and computational efficiency across low and high dimensions, number of objectives, and population sizes. The results indicate that SPINEX-Optimization consistently outperforms most algorithms and excels in managing complex scenarios, especially in high dimensions. The algorithm's capabilities in explainability, Pareto efficiency, and moderate complexity are highlighted through in-depth experiments and visualization methods.
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