In multiobjective optimization, how to calculate the distance to reference point?
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In multiobjective optimization, what does the distance exactly means, is it:
1) The distance from reference point (V) to an individual (Xi) (candidate solution) in the population (decision space).
Euclidean Distance = d(Xi,V)
OR
2) The distance from reference point (V) to the objective vector f(X) in the objective space, where f(Xi) = f(f1(Xi),...fm(Xi)). m is the number of objectives.
Euclidean Distance = d(f(Xi),V)
In this paper "HarrisâÂÂs Hawk Multi-Objective Optimizer for Reference
Point Problems", in the pseudo code, Section 4.2:
a-
Calculate distances to reference point v for each search agent(hawk)
Xi on all objectives ÃÂfm(Xi).
and
b-
Calculate distances to reference point vi for all ÃÂfm(Xi) on each
search agent in group
What is the difference between a and b?
optimization algorithms euclidean-geometry
add a comment |Â
up vote
0
down vote
favorite
In multiobjective optimization, what does the distance exactly means, is it:
1) The distance from reference point (V) to an individual (Xi) (candidate solution) in the population (decision space).
Euclidean Distance = d(Xi,V)
OR
2) The distance from reference point (V) to the objective vector f(X) in the objective space, where f(Xi) = f(f1(Xi),...fm(Xi)). m is the number of objectives.
Euclidean Distance = d(f(Xi),V)
In this paper "HarrisâÂÂs Hawk Multi-Objective Optimizer for Reference
Point Problems", in the pseudo code, Section 4.2:
a-
Calculate distances to reference point v for each search agent(hawk)
Xi on all objectives ÃÂfm(Xi).
and
b-
Calculate distances to reference point vi for all ÃÂfm(Xi) on each
search agent in group
What is the difference between a and b?
optimization algorithms euclidean-geometry
add a comment |Â
up vote
0
down vote
favorite
up vote
0
down vote
favorite
In multiobjective optimization, what does the distance exactly means, is it:
1) The distance from reference point (V) to an individual (Xi) (candidate solution) in the population (decision space).
Euclidean Distance = d(Xi,V)
OR
2) The distance from reference point (V) to the objective vector f(X) in the objective space, where f(Xi) = f(f1(Xi),...fm(Xi)). m is the number of objectives.
Euclidean Distance = d(f(Xi),V)
In this paper "HarrisâÂÂs Hawk Multi-Objective Optimizer for Reference
Point Problems", in the pseudo code, Section 4.2:
a-
Calculate distances to reference point v for each search agent(hawk)
Xi on all objectives ÃÂfm(Xi).
and
b-
Calculate distances to reference point vi for all ÃÂfm(Xi) on each
search agent in group
What is the difference between a and b?
optimization algorithms euclidean-geometry
In multiobjective optimization, what does the distance exactly means, is it:
1) The distance from reference point (V) to an individual (Xi) (candidate solution) in the population (decision space).
Euclidean Distance = d(Xi,V)
OR
2) The distance from reference point (V) to the objective vector f(X) in the objective space, where f(Xi) = f(f1(Xi),...fm(Xi)). m is the number of objectives.
Euclidean Distance = d(f(Xi),V)
In this paper "HarrisâÂÂs Hawk Multi-Objective Optimizer for Reference
Point Problems", in the pseudo code, Section 4.2:
a-
Calculate distances to reference point v for each search agent(hawk)
Xi on all objectives ÃÂfm(Xi).
and
b-
Calculate distances to reference point vi for all ÃÂfm(Xi) on each
search agent in group
What is the difference between a and b?
optimization algorithms euclidean-geometry
optimization algorithms euclidean-geometry
edited Sep 8 at 12:37
asked Sep 8 at 12:30
shdotcom
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