Multi-objective design optimization of heat exchangers using elitist-Jaya algorithm

This paper proposes an elitist-Jaya algorithm for multi-objective design optimization of shell-and-tube and plate-fin heat exchangers. Jaya algorithm is a newly proposed algorithm and it is not having any algorithmic-specific parameters to be set except the common control parameters of number of iterations and population size. Elitist version of the Jaya algorithm is proposed in this paper to simultaneously optimize the total annual cost and effectiveness of the heat exchangers. Two heat exchanger design problems are considered for investigating the performance of the proposed algorithm. The same problems were earlier optimized by other researchers using genetic algorithm and modified teaching-learning-based optimization algorithm. The consequences of common controlling parameters e.g. number of iterations, population size and elite size, on the proposed algorithm’s performance is tested with various combinations of the same. The results of computational experiments proved the superiority of the proposed algorithm over the latest reported approaches used for the design optimization problems of heat exchangers.

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