NSGA-II

Deb, K., Pratap, A., Agarwal, S., Meyari-van, T. (2002) A fast and elitist multiobjective genetic algorithm. NSGA-II. IEEE Trans Evol Comput 6(2), 182-197. 

Froth floatation circuits for mineral processing Maximization of both the recovery of the concentrated ore and valuable mineral content in the concentrated ore. NSGA-II with modified Jumping Gene operator Equality constraint was imposed on total floatation cell volume. Guria et al. (2005a)... 

SMB bioreactor for high fructose syrup by glucose isomerization Maximization of productivity of fructose and minimizing desorbent used. NSGA-II-JG Both operation and design of the SMB bioreactor were optimized. Zhang et al. (2004)... 

Bioreactor for growing Saccharomyces cerevisiae in sugar cane molasses Maximization of profit while minimizing fixed capital investment. NSGA-II, NBI and NNC Performance of NSGA-II, normalized boundary intersection (NBI) and normalized normal constraint (NNC) and the use of bifurcation analysis in decision making are discussed. Sendin el al. (2006)... 

Penicillin V Bioreactor Train Three cases maximization of (a) both penicillin yield and concentration at the end of fermentation, (b) penicillin yield and batch cycle time, and (c) penicillin yield and concentration at the end of fermentation as well as profit. NSGA-II Glucose feed concentration is the decision variable contributing to the Pareto-optimal front. Multiple solution sets producing the same Pareto-optimal front were observed. Lee et al (2007)... 

Maximization of gas yield and minimization of coke formed on the catalyst. NSGA-II-JG The study developed NSGA-II-JG and showed it to be better than NSGA-II for examples smdied. Kasat and Gupta (2003)... 

Terephthalic acid (TA) production Maximization of feed flow rate while minimizing concentration of 4-carboxy-benzaldehyde intermediate in the crude TA. NSGA-II and Neighborhood and Archived GA (NAGA) Mu et al. (2003) employed NSGA-II whereas Mu et al. (2004) used NAGA for four cases of operation optimization with 1 to 6 decision variables. Mu et al. (2003) Mu et al. (2004)... 

Polystyrene using the continuous tower process Maximization of the monomer conversion and minimization of the polydispersity index of the product. NSGA-II A unique solution was obtained instead of a Pareto-optimal set. Bhat et al. (2004)... 

Batch copoly (ethylene- polyoxyethylene terephthalate) reactor Minimization of both reaction time and undesired side products. NSGA-II, NSGA-II-JG andNSGA-II- aJG At near-optimal solutions, NSGA-II-JG was observed to be faster than the other two methods. Kachhap and Guria (2005)... 

Majumdar, S., Mitra, K. and Raha, S. (2005a). Optimized species growth in epoxy polymerization with real-coded NSGA-II, Polymer, 46, pp. 11858-11869. 

Shin et al. (2005) use the controlled NSGA-II (Deb and Goel, 2001) to generate a set of quality DNA sequences. In this study, the quahty of a sequence was achieved by minimizing four objectives the similarity between two sequences in the set, the possible hybridization between sequences in a set, the continuous occurrence of the same base and the possible occurrence of the complementary substring in a sequence. 

Very popular and robust techniques like genetic algorithm (GA) and simulated annealing (SA) are used to solve such problems. The multiobjective forms of these techniques, e.g., NSGA-II (Deb et al., 2002) and MOSA (Suppapitnarm et al., 2000), are quite commonly used these days. These algorithms often require large amounts of computational (CPU) time. Any adaptation to speed up the solution procedure is, thus. 

Fig. 4.1 The Pareto set obtained for the ZDT4 problem (Deb, 2001) using NSGA-II-JG. An additional point, C, is also indicated...

Multi-Objective Elitist Non-Dominated Sorting GA (NSGA-II) and its JG Adaptations... 

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