An Enhanced Particle Swarm Optimisation Algorithm Combined with Neural Networks to Decrease Computational Time

• Published in: Swarm Intelligence Based Optimization pp. 139 - 156
• Main Authors: Leboucher, Cédric, Siarry, Patrick, Le Ménec, Stéphane, Shin, Hyo-Sang, Chelouah, Rachid, Tsourdos, Antonios
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 01.01.2014
• Series: Lecture Notes in Computer Science
• Subjects: Evolutionary Game Theory; Neural Networks; Particle Swarm Optimisation; Swarm Intelligence
• ISBN: 3319129694; 9783319129693
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-12970-9_16

This paper proposes to reduce the computational time of an algorithm based on the combination of the Evolutionary Game Theory (EGT) and the Particle Swarm Optimisation (PSO), named C-EGPSO, by using Neural Networks (NN) in order to lighten the computation of the identified heavy part of the C-EGPSO. This computationally burdensome task is the resolution of the EGT part that consists in solving iteratively a differential equation in order to optimally adapt the direction search and the size step of the PSO at each iteration. Therefore, it is proposed to use NN to learn the solution of this differential equation according to the initial conditions in order to gain a precious time.