
Last chance! 50% off unlimited learning
Sale ends in
Approximates the Pareto-optimal mcMST front of a multi-objective
graph problem by iteratively applying Prim's algorithm for the single-objective
MST problem to a scalarized version of the problem. I.e., the weight vector
mcMSTPrim(instance, n.lambdas = NULL, lambdas = NULL)
[list
] List with component pareto.front
.
[mcGP
]
Multi-objective graph problem.
[integer(1) | NULL
]
Number of weights to generate. The weights are generated equdistantly
in the interval
[numerci
]
Vector of weights. This is an alternative to n.lambdas
.
J. D. Knowles and D. W. Corne, "A comparison of encodings and algorithms for multiobjective minimum spanning tree problems," in Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), vol. 1, 2001, pp. 544–551 vol. 1.
Other mcMST algorithms: mcMSTEmoaBG
,
mcMSTEmoaZhou
g = genRandomMCGP(30)
res = mcMSTPrim(g, n.lambdas = 50)
print(res$pareto.front)
Run the code above in your browser using DataLab