zdt2
Problem definition
Objective function
k = @(x) 1 + 9/29*sum(x(:,2:end),2) h = @(x,y) 1 - (x./y).^2 f = @(x) [x(:,1), k(x).*h(x(:,1),k(x))]
Optimization settings
o = struct% initializing struct
o.d = 30% dimension of decision variable
o.lb = 0% lower bounds
o.ub = 1% upper bounds
Problem properties
| dimension | objectives | smoothness |
| 30 | 2 | - |
Optimization example with nsga2
Algorithm options
o.maxit = 250% number of iterations
o.pop = 100% 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 = 10% frame frequency
optimview('nsga2',info)
References
[1] E. Zitzler, K. Deb, and L. Thiele. "Comparison of Multiobjective Evolutionary Algorithms: Empirical Results", Evolutionary Computation, 8(2):173-195, 2000