ctp1

Problem definition

Objective function
`f  = @(x) [x(:,1), (1+x(:,2)).*exp(-x(:,1)./(1+x(:,2)))]`
Optimization settings
```o = struct	% initializing struct
g1 = @(x) (1+x(:,2)).*exp(-x(:,1)./(1+x(:,2)))./(0.858*exp(-0.541* x(:,1))) -1
g2 = @(x) (1+x(:,2)).*exp(-x(:,1)./(1+x(:,2)))./(0.728*exp(-0.295* x(:,1))) -1
o.g = @(x) g1(x).*(g1(x)<0) + g2(x).*(g2(x)<0)	% constraints
o.d = 2	% dimension of decision variable
o.lb = 0	% lower bounds
o.ub = 1	% upper bounds```

Problem properties

 dimension objectives smoothness 2 2 -

Optimization example with nsga2

Algorithm options
```o.maxit = 50	% number of iterations
o.pop = 300	% number of population ```
Optimization
```rng(0)	% for tractability
[popPos,popFront,popCost,popInfo,traceIt] = nsga2(f,o)	% running minimization  ```
Animation
```rng(0)
[~,~,~,~,~,info] = nsga2(f,o)
info.plot = 'pareto'
info.animate = true	% plot animation
info.animfreq = 4	% frame frequency
optimview('nsga2',info)```

References

[1] K. Deb, "Multiobjective optimization using evolutionary algorithms", Chichester, Wiley, 2002

Resources

ctp1 optimization source code

Related functions

nsga2 | plot | scatter