Non-dominated sorting genetic algorithm

In our earlier works [6,7], we have developed a new optimization procedure based on a genetic algorithm, i.e. the non-dominated sorting genetic algorithm (NSGA), that allows to handle these complex optimization problems. A more detailed description of this global search and optimization technique is available elsewhere [18, 19]. 

Multiobjective optimization of the SMB and Varicol processes by a non-dominated sorting genetic algorithm (NSGA) which does not require any initial guess of the optimum solution was carried out by Zhang et al. [80] who used in that process an objective function that maximizes the feed flow rate (maximum throughput). 

Lee, F. C., Rangaiah, G. P. and Ray, A.K. (2007). Multi-objective optimization of an industrial Penicillin V bioreactor train using non-dominated sorting genetic algorithm, Biotechnol. Bioeng. 98, pp. 586-598. 

Deb, K. and Goel, T. (2001). Controlled elitist non-dominated sorting genetic algorithms for better convergence, in Eckart Zitzler et al. (Eds), First International Conference on Evolutionary Multi-Criterion Optimization. Springer LNCS Vol. 1993 (Zurich, Switzerland), pp. 67-81. 

Based on the hybrid system model and the non-dominated sorting genetic algorithm-II, an optimization approach has been presented regarding nominal reliability, residual reliability on different degradation levels and static parameters as objective parameters. The new method considers the individual state of each component and corresponding failure rates. 

Deb, K., Agrawal, S., Pratab, A., Me-yarivan, T. 2000, A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization NSGA-II. Parallel Problem Solving from Nature VI Conference, Paris, France, 2000, pp. 849-858. 

Srinivas, N., and Deb, K. 1995. Multi-objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation 2 221-248. 

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