ANFIS Coupled Genetic Algorithm Modelling for MIMO Optimization of Flat Plate Heat Sink

This paper introduces a new hybrid approach that uses the output of Taguchi design of experiment (DOE) matrix to train an adaptive neuro-fuzzy inference system (ANFIS) model, and the rules of the ANFIS model are used to perform multiobjective optimization. The proposed approach is applied to optimize the geometry parameters of a flat plate heat sink such as the heat sink is length, width, fin height, base height, fin thickness, and number of fins and the multiobjective functions are minimization of the thermal resistance and emitted radiations of the heat sink. Also, the trained ANFIS model is used to predict the performance of the multiple outputs given the combination of input parameters. The multiple responses such as emitted radiations and thermal resistance are optimized by 22.18% with respect to the original design. Also, this method serves to reduce the search space for a given problem. Using the reduced search space, genetic algorithm (GA) was used at the second level of optimization to further improve the performance by upto 34.12% compared to the original design. Thus, the proposed Taguchi-based ANFIS modelling can either be used as a simple and standalone approach for optimization or can be combined with GA in the next level for any kind of MIMO parameter optimization problems.