Particle Swarm optimization (PSO) is a robust stochastic evolutionary computation technique which is based on the movement and intelligence of swarms. In this paper the PSO algorithm is modified to improve its performance in a class of design applications in heat transfer. The developed approach includes a new term called a chaotic acceleration factor (Ca) into the algorithm, which enhances its convergence rate and its accuracy. The modified PSO is empirically tested with well-known benchmark functions. Next it is applied in plate-fin design with the objective of dissipating the maximum heat generation from an electronic component by minimizing the entropy generation rate to obtain the highest heat transfer efficiency.

This content is only available via PDF.
You do not currently have access to this content.