Tushar Goel Kalyanmoy Deb One of most popular MOGA algorithms Used in Matlabs gamultobj tushargufledu 2 Pareto optimal front Usual approaches weighted sum strategy multiple ID: 684919
Download Presentation The PPT/PDF document "Elitist Non-dominated Sorting Genetic Al..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Elitist Non-dominated Sorting Genetic Algorithm: NSGA-II
Tushar Goel (Kalyanmoy Deb)
One of most popular MOGA algorithms. Used in Matlab’s gamultobjSlide2
tusharg@ufl.edu
2
Pareto optimal front
Usual approaches: weighted sum strategy,
multiple-
constraints modeling
Alternative: Multi-objective GA
Algorithm requirements: ConvergenceSpread
Min f
2
Min f
1Slide3
tusharg@ufl.edu
3
Ranking
Children and parents are combined.
N
on-dominated points belong to first rank.
The non-dominated solutions from the remainder are in second rank, and so on.
f
2
f
1Slide4
tusharg@ufl.edu
4
Elitism
Elitism: Keep the best individuals from the parent and child population
f
2
f
1
Parent
ChildSlide5
tusharg@ufl.edu
5
Niching for last rank
Niching is an operator that gives preference to solutions that are not crowded
Crowding distance
c = a + b
Solutions from last rank are selected based on niching
f
2
f
1
a
bSlide6
tusharg@ufl.edu
6
Flowchart of NSGA-II
Begin: initialize population (size N)
Evaluate objective functions
Selection
Crossover
Mutation
Evaluate objective function
Stopping criteria met?
Yes
No
Child population created
Rank population
Combine parent and child populations, rank population
Select N individuals
Elitism
Report final population and StopSlide7
Problems NSGA-II
Sort all the individuals in slide 4 into ranks, and denote the rank on the figure in the slide next to the individual.Describe how the 10 individuals were selected, and check if any individuals were selected based on crowding distance.
tusharg@ufl.edu
7