xh4 / MOEA.jl

Multi-Objective Evolutionary Algorithms in Julia

MOEA

Multi-Objective Evolutionary Algorithms in Julia

Algorithms

• NSGA-II
• MOEA/D
• MOEA/D-DE
• MOEA/D-DRA
• MOEA/D-AWA
• SMS-EMOA

Problems

• ZDT
• DTLZ
• WFG

Indicators

• IGD
• Hypervolume

GA

ga = GA(populationSize=100,
	selection=susinv,
	mutation=BGA(ones(2)),
	crossover=DC)

result = optimize(sphere,
                  ga,
		  population = [Individual([2.0, 2.0]) for i in 1:ga.populationSize])

MOEA/D

zdt, bounds, truepf = ZDT1()

constraints = BoxConstraints(bounds[1,:], bounds[2,:])

method = MOEAD(N=100)

moead = optimize(zdt,
		 moead,
		 constraints = constraints,
		 population = [Individual(rand(30)) for i in 1:100])

Test

julia -e 'import Pkg; Pkg.activate(pwd()); Pkg.test()'