This paper addresses some limitations of the Cross-Entropy (CE) method in solving high-dimensional and complex optimization problems, particularly when using Gaussian distributions. We propose an alternative law-smooth update scheme to overcome these challenges and enhance convergence. This approach is well suited to the use of a variety of sampling laws, the laws of the exponential family, including Dirichlet distributions, which can be used for the optimization of permutation-invariant criteria. Specifically, we focus on permutation-invariant optimization problems, which often present difficulties due to multiple global optima and complex criterion variations. To resolve these issues, we introduce two approaches: one uses sampling laws to generate unique representatives for equivalent elements within permutations, while the other utilizes a balanced bijective mapping from a vector space to element classes within permutations. These methods are explored theoretically. Applications to benchmark functions (Rosenbrock and permutation-modified Rosenbrock) is presented. Finally is presented a real-world application to multispectral band selection for anomaly detection.