Territorial Differential Meta-Evolution (TDME) is an efficient, versatile, and reliable algorithm for seeking all the global or desirable local optima of a multivariable function. It employs a progressive niching mechanism to optimize even challenging, highdimensional functions with multiple global optima and misleading local optima. This article introduces TDME and uses standard and novel benchmark problems to quantify its advantages over HillVallEA, which is the best-performing algorithm on the standard benchmark suite that has been used by all major multimodal optimization competitions since 2013. TDME matches HillVallEA on that benchmark suite and categorically outperforms it on a more comprehensive suite that better reflects the potential diversity of optimization problems. TDME achieves that performance without any problem-specific parameter tuning.

中文翻译：

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区域微分元进化：一种寻求多变量函数所有理想最优值的算法。

区域差分元进化 (TDME) 是一种高效、通用且可靠的算法，用于寻找多变量函数的所有全局或理想的局部最优值。它采用渐进式的利基机制来优化具有多个全局最优值和误导性局部最优值的具有挑战性的高维函数。本文介绍了 TDME，并使用标准和新颖的基准问题来量化其相对于 HillVallEA 的优势，HillVallEA 是标准基准套件上性能最佳的算法，自 2013 年以来已被所有主要多模态优化竞赛使用。TDME 在该基准套件上与 HillVallEA 相匹配并且在更全面的套件上明显优于它，更好地反映了优化问题的潜在多样性。