Abstract
Evolutionary multi-objective optimization (EMO) found applications in all fields of science and engineering. Chemical engineering discipline is no exception. Literature abounds on EMO with a variety of algorithms proposed by a few dedicated researchers. The Nondominated Sorting Genetic Algorithm (NSGA-III) is the latest addition to the family of EMO. NSGA-III claims to have solved multi and many-objective optimization problems up to 15 objective functions. On the other hand, during the last 2 decades, chemical engineering has witnessed many applications of multi-objective optimization algorithms such as NSGA-II. In a first-of-its-kind study, this paper exploits the power and versatility of the NSGA-III to solve a four-objective optimization problem occurring in refinery profit planning. NSGA-III is eminently suitable for this class of problems. We applied NSGA-III to this problem and obtained the full set of pareto solutions for the four-objective problem. We also observed that they are dominated solutions when compared to the FNLGP and others. The ratio of HV/IGD was proposed to measure the quality of the solutions obtained in a run. It can be applied to solve other many-objective optimization problems in Chemical Engineering.
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Madhav, V., Huq, S.TU., Ravi, V. (2021). Refinery Profit Planning via Evolutionary Many-Objective Optimization. In: Gunjan, V.K., Zurada, J.M. (eds) Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough. Studies in Computational Intelligence, vol 956. Springer, Cham. https://doi.org/10.1007/978-3-030-68291-0_3
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