Optimal Thermal Distribution by using Inverse Genetic Algorithm Optimization Technique

Authors

  • A. K. Abubakar Centre for Artificial Intelligence & Robotic (CAIRO), Department of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
  • F. S. Ismail Centre for Artificial Intelligence & Robotic (CAIRO), Department of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
  • N. W. B. Hisham Centre for Artificial Intelligence & Robotic (CAIRO), Department of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.

Keywords:

Components Placement Design, Fitness Function, Inverse Genetic Algorithm, Optimization

Abstract

Optimal arrangement of components on printed circuit board (PCB) has become a basic necessity so as to have effective management of heat generation and dissipation. In this work, Inverse Genetic Algorithm (IGA) optimization has been adopted in order to achieve this objective. This paper proposes IGA search engine to optimize the thermal profile of components based on thermal resistance network and to minimize the area of PCB. Comparison between the proposed IGA and the conventional GA (FGA) performances are extensively analyzed. Unlike the conventional FGA, the IGA approach allows the user to set the desired fitness, so that the GA process will try to approach these set values. A reduction in the overall computational time and the freedom of choosing a desired fitness are the major advantages of IGA over FGA. From the simulation results, the IGA has successfully minimized the thermal profile and area of PCB by 0.78% and 1.28% respectively. The computational time has also been minimized by 15.56%.

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Published

2016-12-01

How to Cite

Abubakar, A. K., Ismail, F. S., & Hisham, N. W. B. (2016). Optimal Thermal Distribution by using Inverse Genetic Algorithm Optimization Technique. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(11), 15–21. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1404

Issue

Vol. 8 No. 11: Artificial Intelligence and Robotics

Section

Articles

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