Multi-objective design optimization of a plate-fin heat sink using a teaching-learning-based 
optimization algorithm

Abstract

This paper presents the multiobjective design optimization of a plate-fin heat sink equipped 
with flow-through and impingement-flow air cooling systems using teaching-learning-based 
optimization algorithm. Two objective functions known as entropy generation rate and material 
cost with five constraints have been taken to measure the performance of the heat sink. Number 
of fins, height of fins, spacing between two fins and oncoming air velocity are considered as 
the design variables. The dynamic heat dissipation performance of plate-fin heat sink is 
investigated using finite element software ANSYS 12.1. The results show the better or competitive 
performance of the TLBO algorithm over the other optimization algorithms considered by the previous 
researchers for the same problem.