Finding the global minimum of a nonconvex optimization problem is a notoriously hard task
appearing in numerous applications, from signal processing to machine learning. Simulated …

This paper discusses the relationship between data science and population-based
algorithms, which include swarm intelligence and evolutionary algorithms. We reviewed two …

In this paper, we propose a stochastic search algorithm for solving general optimization
problems with little structure. The algorithm iteratively finds high quality solutions by …

In this paper, we provide a brief introduction to particle filter optimization (PFO). The particle
filter (PF) theory has revolutionized probabilistic state filtering for dynamic systems, while the …

Population annealing is a powerful sequential Monte Carlo algorithm designed to study the
equilibrium behavior of general systems in statistical physics through massive parallelism. In …

By facilitating the generation of samples from arbitrary probability distributions, Markov
Chain Monte Carlo (MCMC) is, arguably, the tool for the evaluation of Bayesian inference …

For Copula models, the likelihood function could be multi-modal, and some traditional
optimization algorithms such as simulated annealing (SA) may get stuck in the local mode …

In this study, we propose a novel topology-informed search strategy for derivative-free
metaheuristic optimization, enhancing both Simulated Annealing (SA) and Particle Swarm …

We propose a number‐theory‐based quantized mathematical optimization scheme for
various NP‐hard and similar problems. Conventional global optimization schemes, such as …

Abstract Particle Filters (PFs) have been successfully employed for monocular 3D model-
based tracking of rigid objects. However, these filters depend on the computation of …