TY - GEN
T1 - An analysis of PSO inertia weight effect on swarm robot source searching efficiency
AU - Majid, M. H.A.
AU - Arshad, A. M.R.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/22
Y1 - 2017/12/22
N2 - Particle swarm optimization (PSO) is a well-known metaheuristic and population based optimization algorithm. PSO has been used in many science and engineering applications. One of the examples include as a source searching algorithm in swarm robotics. In general, PSO searching performance is determined by its parameters include inertia weight and acceleration coefficients. In this paper, a comparison analysis of inertia weight adjustment strategies on the source searching capability is presented. Several methods of adjusting the inertia weight are studied such as constant inertia weight, randomized inertia weight, linearly decreasing inertia weight, increasing inertia weight, and adaptive inertia weight. The analysis is performed by keeping other PSO parameters including the acceleration coefficients constant. The comparison is benchmarked based on a few performance indexes:convergence time, accuracy, percentage of search success and smoothness of trajectory. The comparison results presented in this study are useful as a basic guideline for development of a better PSO based source searching algorithm for swarm robotics in the future.
AB - Particle swarm optimization (PSO) is a well-known metaheuristic and population based optimization algorithm. PSO has been used in many science and engineering applications. One of the examples include as a source searching algorithm in swarm robotics. In general, PSO searching performance is determined by its parameters include inertia weight and acceleration coefficients. In this paper, a comparison analysis of inertia weight adjustment strategies on the source searching capability is presented. Several methods of adjusting the inertia weight are studied such as constant inertia weight, randomized inertia weight, linearly decreasing inertia weight, increasing inertia weight, and adaptive inertia weight. The analysis is performed by keeping other PSO parameters including the acceleration coefficients constant. The comparison is benchmarked based on a few performance indexes:convergence time, accuracy, percentage of search success and smoothness of trajectory. The comparison results presented in this study are useful as a basic guideline for development of a better PSO based source searching algorithm for swarm robotics in the future.
KW - inertia weight
KW - particle swarm optimization
KW - robotic source searching
KW - source searching
KW - swarm robotics task
UR - http://www.scopus.com/inward/record.url?scp=85048197430&partnerID=8YFLogxK
U2 - 10.1109/I2CACIS.2017.8239053
DO - 10.1109/I2CACIS.2017.8239053
M3 - Conference Proceeding
AN - SCOPUS:85048197430
T3 - Proceedings - 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems, I2CACIS 2017
SP - 173
EP - 178
BT - Proceedings - 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems, I2CACIS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2017
Y2 - 21 October 2017
ER -