Multi-objective optimization of phthalic anhydride catalytic reactor using genetic algorithm with simulated binary jumping genes operator

Vibhu Trivedi, Manojkumar Ramteke

Department of Chemical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110 016, India.

DOI:

https://doi.org/10.7494/cmms.2015.1.0510

Abstract:

Multi-objective optimization problems of chemical industry have been efficiently solved by evolutionary algorithms (EAs). However, due to high computational costs, different concepts are introduced in evolutionary framework for the improvement of convergence speed. One such concept is ‘jumping genes’ which has been adapted in binary-coded genetic algorithm and found to be improving the performance of the algorithm significantly. However, its adaptation in realcoded form lacked the similar success. In an attempt to fill this gap, a new jumping genes operator has been recently developed for real-coded elitist non-dominated sorting genetic algorithm (RNSGA-II), namely, simulated binary jumping genes (SBJG). This work aims at exploring the utility of SBJG for solving real-life industrial optimization problems using a case study of multi-objective optimization (MOO) of an industrial phthalic anhydride (PA) catalytic reactor. The results obtained are found to be converging faster than RNSGA-II and its other existing jumping genes adaptations for both two and three-objective formulations.

Cite as:

Trivedi, V., & Ramteke, M. (2015). Multi-objective optimization of phthalic anhydride catalytic reactor using genetic algorithm with simulated binary jumping genes operator. Computer Methods in Materials Science, 15(1), 111-117. https://doi.org/10.7494/cmms.2015.1.0510

Article (PDF):