Coarse-grained force fields (CG FFs) such as the Martini model entail a predefined, fixed set
of Lennard-Jones parameters (building blocks) to model virtually all possible nonbonded …

In this paper, an improved variant of the Self-Regulating Particle Swarm Optimization
(SRPSO) algorithm is proposed that further enhances the performance of the basic SRPSO …

We propose a new particle swarm optimization algorithm for problems where objective
functions are subject to zero-mean, independent, and identically distributed stochastic noise …

Human learning Optimization (HLO) is an emergent promising meta-heuristic algorithm
which uses the random learning operator, the individual learning operator, and the social …

In this paper, we introduce the concept of population density in PSO, and accordingly, we
discuss the relationship between the search capability of PSO and the population density …

Although particle swarm optimization (PSO) is a powerful evolutionary algorithm for solving
nonlinear optimization problems in deterministic environments, many practical problems …

Purpose This paper aims to propose comparing the performance of three algorithms based
on different population-based heuristics, particle swarm optimization (PSO), artificial bee …

A novel approach to incorporating Machine Learning into optimization routines is presented.
An approach which combines the benefits of ML, optimization, and meta-model searching is …

Optimization under uncertainty is a key problem in order to solve complex system design
problem while taking into account inherent physical stochastic phenomena, lack of …

VANET means vehicular ad-hoc network. The routing protocol ant colony optimization (ACO)
is a swarm intelligence which is based on the behavior of the ant, the ACO algorithm …