International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Harish Garg1 and S. P. Sharma

Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India


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ABSTRACT


In a real life situation, due to the complexity of the industrial systems and non-linearity of their behavior, it is very difficult to achieve optimum performance of system for desired industrial goals using uncertain, vague and imprecise data. Herein, an approach has been proposed through which the behavior of the system is analyzed in the form of well- known six reliability indices by using triangular fuzzy numbers which allow the consideration of expert opinions, linguistic variables, operating conditions in reliability information. Using their behavior analysis a fuzzy multi-objective optimization problem (FMOOP) has been formulated. Due to the conflicting nature of the multiple objectives, the decision making is difficult and it leads to the Pareto optimal solutions instead of single optimal solutions. Many evolutionary algorithms (EAs) already exist in the literature for solving a multi-objective optimization problem (MOOP), and are termed as multi-objective evolutionary algorithms (MOEAs). Particle swarm optimization (PSO) is one of such MOEA which demonstrates the ability to identity a Pareto-optimal front efficiently. Here, a crisp optimization problem is reformulated from FMOOP by taking into account the preference of decision maker (DM) and then PSO is applied to solve the resulting fuzzified MOOP. The presented approach is applied in order to solve the multi-objective series-parallel system reliability optimization problem for a crystallization unit of a urea plant.


Keywords: Multi-objective optimization; fuzzy optimization; PSO; Pareto-optimal solution, lambda-tau.


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ARTICLE INFORMATION




Accepted: 2011-10-03
Publication Date: 2011-12-01


Cite this article:

Garg, H., Sharma, S.P. 2011. Multi-objective optimization of crystallization unit in a fertilizer plant using particle swarm optimization. International Journal of Applied Science and Engineering, 9, 261–276. https://doi.org/10.6703/IJASE.2011.9(4).261


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