Fonseca-Fleming
Problem definition
Objective function
sqrd = sqrt(3)
f = @(x) 1 - [ exp(-sum((x-1/sqrd).^2,2)),...
exp(-sum((x+1/sqrd).^2,2)) ]
Optimization settings
o = struct% initializing struct
o.d = 3% dimension of decision variable
o.lb = -4*ones(1,3)% lower bounds
o.ub = 4*ones(1,3)% upper bounds
Problem properties
| dimension | objectives | smoothness |
| 3 | 2 | - |
Optimization example with nsga2
Algorithm options
o.maxit = 40% number of iterations
o.pop = 40% 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.animstart = 20% iteration to start animation
optimview('nsga2',info)
References
[1] C. Fonseca, P. Fleming, "Genetic algorithms for multiobjective optimization: Formulation, discussion
and generalization. Proceedings of the Fifth International Conference on Genetic Algorithms", San
Mateo, California, 416423, 1993