This paper tackles the age-old question of derivatefree optimization in neural networks. This
paper introduces AdaSwarm, a novel derivative-free optimizer to have similar or better …

This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even
better performance than the Adam optimizer adopted in neural networks. In order to support …

Nowadays, the Adam algorithm has become one of the most popular optimizers to train feed-
forward neural networks because it takes the best features of other gradient-based …

Bare-bones particle swarm optimization (BPSO) is attractive since it is easy to implement
and parameter-free. However, it suffers from premature convergence because of quickly …

Particle Swarm Optimization (PSO) has attracted many researchers attention to solve variant
benchmark and real-world optimization problems because of its simplicity, effective …

Particle swarm optimization (PSO), which is one of the leading swarm intelligence
algorithms, dominates other optimization algorithms in some fields but, it also has the …

This paper generalizes the optimized gradient method (OGM) Y. Drori and M. Teboulle,
Math. Program., 145 (2014), pp. 451--482, D. Kim and JA Fessler, Math. Program., 159 …

Many scientific, engineering and economic problems involve the optimisation of a set of
parameters. These problems include examples like minimising the losses in a power grid by …

Abstract Particle Swarm Optimization (PSO) is a widely-used nature-inspired optimization
technique in which a swarm of virtual particles work together with limited communication to …

In this paper, a new approach to momentum particle swarm optimization is proposed. We
design a particle swarm optimizer that converges faster than the currently available …