# Improvement of PSO algorithm by memory based gradient search - application in inventory management

arXiv: https://arxiv.org/abs/1410.5652 · HTML: https://mlsanity.com/paper/1410.5652/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2014.

## Built on by (most cited first, 2 of 2)

- [Parameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization](https://mlsanity.com/paper/2405.10750/) (2024)
- [Implementation of Parallel Simplified Swarm Optimization in CUDA](https://mlsanity.com/paper/2110.01470/) (2021)

## Abstract (arXiv)

Advanced inventory management in complex supply chains requires effective and robust nonlinear optimization due to the stochastic nature of supply and demand variations. Application of estimated gradients can boost up the convergence of Particle Swarm Optimization (PSO) algorithm but classical gradient calculation cannot be applied to stochastic and uncertain systems. In these situations Monte-Carlo (MC) simulation can be applied to determine the gradient. We developed a memory based algorithm where instead of generating and evaluating new simulated samples the stored and shared former function evaluations of the particles are sampled to estimate the gradients by local weighted least squares regression. The performance of the resulted regional gradient-based PSO is verified by several benchmark problems and in a complex application example where optimal reorder points of a supply chain are determined.