@Article{iasc.2020.013921,
AUTHOR = {Mehiar, D.A.F., Azizul, Z.H., Loo, C.K.},
TITLE = {QRDPSO: A New Optimization Method for Swarm Robot Searching and Obstacle Avoidance in Dynamic Environments},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {3},
PAGES = {447--454},
URL = {http://www.techscience.com/iasc/v26n3/40004},
ISSN = {2326-005X},
ABSTRACT = {In this paper we show how the quantum-based particle swarm optimization 
(QPSO) method is adopted to derive a new derivation for robotics application in 
search and rescue simulations. The new derivation, called the Quantum Robot 
Darwinian PSO (QRDPSO) is inspired from another PSO-based algorithm, the 
Robot Darwinian PSO (RDPSO). This paper includes comprehensive details on 
the QRDPSO formulation and parameters control which show how the swarm 
overcomes communication constraints to avoid obstacles and achieve optimal 
solution. The results show the QRDPSO is an upgrade over RDPSO in terms of 
convergence speed, trajectory control, obstacle avoidance and connectivity 
performance of the swarm.},
DOI = {10.32604/iasc.2020.013921}
}