tnk
Problem definition
Objective function
f = @(x) [x(:,1) x(:,2)]
Optimization settings
o = struct% initializing struct
g1 = @(x) sum(x.^2,2) - 1 - 0.1*cos(16*atan(x(:,1)./x(:,2))) g2 = @(x) 0.5 - sum((x-0.5).^2,2) 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 = pi% upper bounds
Problem properties
| dimension | objectives | smoothness |
| 2 | 2 | - |
Optimization example with nsga2
Algorithm options
o.maxit = 200% 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] M. Tanaka, "GA-based decision support system for multi-criteria opimization", Proceeding of the International Conference on Systems, Man and Cybernetics-2, pp. 1556-1561, 1995