Scalability of optimization algorithms is a major challenge in coping with the ever-growing
size of optimization problems in a wide range of application areas from high-dimensional …

Computationally efficient algorithms for large-scale black-box optimization have become
increasingly important in recent years due to the growing complexity of engineering and …

Identification of variable interaction is essential for an efficient implementation of a divide-
and-conquer algorithm for large-scale black-box optimization. In this paper, we propose an …

We propose a population-based Evolutionary Stochastic Gradient Descent (ESGD)
framework for optimizing deep neural networks. ESGD combines SGD and gradient-free …

Evolution strategies are a class of evolutionary algorithms for black-box optimization and
achieve state-of-the-art performance on many benchmarks and real-world applications …

The CMA-ES is one of the most powerful stochastic numerical optimizers to address difficult
black-box problems. Its intrinsic time and space complexity is quadratic—limiting its …

Salp swarm algorithm (SSA) is a unique swarm intelligent algorithm widely used for various
practical applications due to its simple framework and good optimization performance …

The covariance matrix adaptation evolution strategy (CMA-ES) is a popular method to deal
with nonconvex and/or stochastic optimization problems when gradient information is not …

This article deals with Gaussian process surrogate models for the Covariance Matrix
Adaptation Evolutionary Strategy (CMA-ES)—several already existing and two by the …

Particle swarm optimization (PSO) suffers from delayed convergence and stagnation in the
local optimal solution, as do most meta-heuristic algorithms. This study proposes a time …