Study of multi-objective optimization and its implementation using NSGA-II

Das, Rahul Kumar and Samal, Chittaranjan and Mallick, Suryakant (2007) Study of multi-objective optimization and its implementation using NSGA-II. BTech thesis.



This project investigates the Multi-objective optimization strategies and their solutions using Multi-objective evolutionary algorithms (MOEAs). Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing are criticized mainly for their; a) computational complexity, b) lack of elitism, c) need for specifying sharing parameter. In this paper the Non- Dominated Sorting Genetic Algorithm (NSGA) is studied and NSGA-II as proposed by Deb et. al. has been implemented, which alleviates the above three difficulties. In this study different objectives have been considered with different variables and constraints. The algorithm yielded satisfactory simulation results in all the different cases. The effect of the genetic parameters on the Pareto-Optimal front in all the cases has been studied. The results show that NSGA-II find much better spread of solutions and better convergence near the true pareto optimal front compared to other elitist MOEAs.

Item Type:Thesis (BTech)
Uncontrolled Keywords:NSGA-II, MOEAs
Subjects:Engineering and Technology > Electrical Engineering
Divisions: Engineering and Technology > Department of Electrical Engineering
ID Code:4281
Deposited By:Hemanta Biswal
Deposited On:05 Jul 2012 16:18
Last Modified:20 Dec 2013 14:05
Supervisor(s):Nanda, P K

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