| Summary: | Heat exchangers are usually designed using a sophisticated process of trial-and-error to find proper values of unknown parameters which satisfy given requirements. Recently, the design of heat exchangers using evolutionary optimization algorithms has received attention. The major aim of the present study is to propose an improved Gaussian quantum-behaved particle swarm optimization (GQPSO) algorithm for enhanced optimization performance and its verification through application to a multivariable thermal-economic optimization problem of a crossflow plate–fin heat exchanger (PFHE). Three single objective functions: the number of entropy generation units (<i>NEGUs</i>), total annual cost (<i>TAC</i>), and heat exchanger surface area (<i>A</i>), were minimized separately by evaluating optimal values of seven unknown variables using four different PSO-based methods. By comparing the obtained best fitness values, the improved GQPSO approach could search quickly for better global optimal solutions by preventing particles from falling to the local minimum due to its modified local attractor scheme based on the Gaussian distributed random numbers. For example, the proposed GQPSO could predict further improved best fitness values of 40% for <i>NEGUs</i>, 17% for <i>TAC</i>, and 4.5% for <i>A</i>, respectively. Consequently, the present study suggests that the improved GQPSO approach with the modified local attractor scheme can be efficient in rapidly finding more suitable solutions for optimizing the thermal-economic problem of the crossflow PFHE.
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