American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: Editor-in-chief: Mohamed Seddeek
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American Journal of Applied Mathematics and Statistics. 2013, 1(6), 110-116
DOI: 10.12691/ajams-1-6-1
Open AccessArticle

Multi-Objective Optimization Firefly Algorithm Applied to (Bio)Chemical Engineering System Design

Fran Sérgio Lobato1 and Jr Valder Steffen2,

1School of Chemical Engineering, Federal University of Uberlândia, Uberlândia, Brazil

2School of Mechanical Engineering, Federal University of Uberlândia, Uberlândia, Brazil

Pub. Date: November 19, 2013

Cite this paper:
Fran Sérgio Lobato and Jr Valder Steffen. Multi-Objective Optimization Firefly Algorithm Applied to (Bio)Chemical Engineering System Design. American Journal of Applied Mathematics and Statistics. 2013; 1(6):110-116. doi: 10.12691/ajams-1-6-1


Modern engineering problems are often composed by objectives that must be taken into account simultaneously for better design performance. Normally, these objectives are conflicting, i.e., an improvement in one of them does not lead, necessarily, to better results for the other ones. To overcome this difficulty, many methods to solve multi-objective optimization problems (MOP) have been proposed. The MOP solution, unlike the single objective problems, is given by a set of non-dominated solutions that form the Pareto Curve, also known as Pareto Optimal. Among the MOP algorithms, we can cite the Firefly Algorithm (FA). FA is a bio-inspired method that mimics the patterns of short and rhythmic flashes emitted by fireflies in order to attract other individuals to their vicinities. For illustration purposes, in the present contribution the FA, associated with the Pareto dominance criterion and the anti-stagnation operator, is applied to (bio)chemical engineering system design. The first one is related to the alkylation process optimization; the second deals with the optimization of batch stirred tank reactor. The sensitivity analysis of some relevant parameters of the algorithm is performed and compared with the Non-dominated Sorting Genetic Algorithm (NSGA II). The results indicate that the proposed approach characterizes an interesting alternative for multi-objective optimization design.

multi-objective optimization firefly algorithm (Bio)chemical engineering system design

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