Adaptive Range Genetic Algorithm: A hybrid optimization approach and its application in the design and economic optimization of Shell-and-Tube Heat Exchanger

摘要

Several optimization algorithms have been developed in the past for application in a variety of complex real world problems. Herein, a new hybrid variant of Genetic Algorithm (GA), utilizing the sample space reduction technique of Cohort Intelligence (CI) Algorithm, referred to as Adaptive Range Genetic Algorithm (ARGA) is presented and discussed. The correctness of the proposed algorithm is established by solving 50 standard test functions. Also, the practical applicability of the proposed algorithm is demonstrated by applying it to the design and economic optimization problem of a Shell and Tube Heat Exchanger. Heat exchangers are critical industrial components used in a wide range of industrial applications. Owing to their high manufacturing costs and complicated design criteria, their economic efficiency of operation depends heavily on optimization of their design. A relative assessment, comparing the performance of the proposed method of solution to other existing algorithmic methods, is conducted. The quality and robustness of the proposed solution method highlights its adeptness in solving real world computational problems in core mechanical engineering applications.