| Summary: | Considering the current trend of compact designs which are mostly multiobjective in nature, proper arrangement of components has become a basic necessity so as to have optimal management of heat generation and dissipation. In this work, Inverse Genetic Algorithm (IGA) optimization has been adopted in order to achieve optimal placement of components on printed circuit board (PCB). The objective functions are the PCB area and temperature of each component while the constraint parameters are; to avoid the overlapping of components, the maximum allowable PCB area is 2(120193.4)mm2 , thermal connections were internally set, and the manufacturer allowable temperature for the ICs must be more than the components optimal temperature. In the conventional Forward Genetic Algorithm (FGA) optimization, the individual fitness of components are generated through the GA process. The IGA approach on the other hand, allows the user to set the desired fitness, so that the GA process will try to approach these set values. Hence, the IGA has two major advantages over FGA; the first being a reduction in the overall computational time and the other is the freedom of choosing the desired fitness (i.e. ability to manipulate the GA output). The objectives of this work includes; development of an IGA search Engine, minimization of the thermal profile of components based on thermal resistance network and the area of PCB, and comparison of the proposed IGA and FGA performances. From the simulation results, the IGA has successfully minimized the thermal profile and area of PCB by 0.78% and 1.28% respectively. The CPU-time has also been minimised by 15.56%.
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