TY - GEN
T1 - Archive-based swarms
AU - Rodrigues, Nishant
AU - Mohan, Chilukuri
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/7/8
Y1 - 2020/7/8
N2 - The Particle Swarm Optimization (PSO) algorithm updates each individual's velocity and position using its own prior best position and the best position found so far by any particle. Effective search for global optima can benefit if each particle may utilize additional historical information regarding the quality of previously visited positions in its proximity. We present an approach for doing so, using an archive containing some prior particle positions, analogous to the archive used in some multi-objective optimization algorithms. A specific instance of this approach is the proposed Fitness-Distance-Ratio Archive-Based Swarm Optimization algorithm, shown to outperform PSO and three other variants, when applied to high-dimensional neural network training problems.
AB - The Particle Swarm Optimization (PSO) algorithm updates each individual's velocity and position using its own prior best position and the best position found so far by any particle. Effective search for global optima can benefit if each particle may utilize additional historical information regarding the quality of previously visited positions in its proximity. We present an approach for doing so, using an archive containing some prior particle positions, analogous to the archive used in some multi-objective optimization algorithms. A specific instance of this approach is the proposed Fitness-Distance-Ratio Archive-Based Swarm Optimization algorithm, shown to outperform PSO and three other variants, when applied to high-dimensional neural network training problems.
KW - Archive
KW - Artificial neural networks
KW - Evolutionary machine learning
KW - Fitness-distance ratio
KW - Particle swarm optimization
KW - Premature convergence
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85089751154&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089751154&partnerID=8YFLogxK
U2 - 10.1145/3377929.3398112
DO - 10.1145/3377929.3398112
M3 - Conference contribution
AN - SCOPUS:85089751154
T3 - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
SP - 1460
EP - 1467
BT - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2020 Genetic and Evolutionary Computation Conference, GECCO 2020
Y2 - 8 July 2020 through 12 July 2020
ER -