# Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs

arXiv: https://arxiv.org/abs/2010.02039 · HTML: https://mlsanity.com/paper/2010.02039/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

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

- [Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization](https://mlsanity.com/paper/2104.10033/) (2021)
- [Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems](https://mlsanity.com/paper/2206.07655/) (2022)

## Abstract (arXiv)

This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm including the cognitive and social coherence, and thus resulting in better solutions. Results from extensive simulations with existing methods show that the proposed MPSO improves the detection performance by 24% and time performance by 4.71 times compared to the original PSO, and moreover, also outperforms other state-of-the-art metaheuristic optimization algorithms including the artificial bee colony (ABC), ant colony optimization (ACO), genetic algorithm (GA), differential evolution (DE), and tree-seed algorithm (TSA) in most search scenarios. Experiments have been conducted with real UAVs in searching for a dynamic target in different scenarios to demonstrate MPSO merits in a practical application.