nsga2
nsga2 solves for the Pareto frontier in multi-objective problems using the Non-dominated Sorting Genetic Algorithm II.
[popPos,popFront,popCost,popInfo,traceIt,info] = nsga2(f,o,objmin)
Outputs:
[popPos,popFront,popCost,popInfo,traceIt,info] = nsga2(f,o,objmin)
- f: function handle
- o: optimization options
- objmin: logical vector to define if fi(x) is a minimization (true) or maximization(false) objective
Options | Description | Values | Default values |
d | Dimension | positive integer | number of variables |
g | Constrains | function handle | [] |
lb | Lower bounds | double | realmin/100 |
ub | Upper bounds | double | realmax/100 |
display | Information display level | 'iter' | 'iter' |
pop | Population size | positive integer | 10 |
maxit | Maximum Number of Iterations | positive integer | 10 |
- popPos: contains position for the entire population (each row corresponds to one individual)
- popFront: pareto frontier indices (popPos(popFront,:) returns the Pareto frontier positions)
- popCost: the cost for each individuals
- popInfo: additional information for each individual: rank, crowding distance, feasibility
- traceIt: cell array storing all the above outputs at each iteration
- info: struct object with information for the animation
Animation
Call optimview('nsga2',info), being popInfo the forth output of nsga2. Some animation options can be specified appending them to the struct object info.Options | Description | Values | Default values |
plot | Plots pareto front or position of the population | 'pos', 'pareto' | {'pos' 'pareto'} |
animate | Generates the animation | logical | true |
animfreq | Frame frequency | positive integer | 1 |
animstart | Inicial frame to start animation | positive integer | 1 |
References
[1] K. Deb, Associate Member, IEEE, A. Pratap, S. Agarwal, T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Transactions on Evolutionary computation, Vol. 6, NO. 2, 2002.
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