Adaptive Range Genetic Algorithm: A hybrid optimization approach and its application in the design and economic optimization of Shell-and-Tube Heat Exchanger. (October 2019)

• Record Type: Journal Article
• Authors: Iyer, Vidyadhar H.; Mahesh, S.; Malpani, Rohit; Sapre, Mandar; Kulkarni, Anand J.
• Is Part Of: Engineering applications of artificial intelligence. Volume 85(2019)
• Page Start: 444
• Page End: 461
• Publication Date: 2019-10
• Subjects: Adaptive Range Genetic Algorithm -- Genetic Algorithm -- Cohort Intelligence Algorithm -- Shell and Tube Heat Exchanger -- Optimization; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.07.001
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.