程序代写 Constraint Handling — Objective Function – cscodehelp代写
Constraint Handling — Objective Function
Objective function defines the cost of a solution. (N−1 )
minimise totalDistance(x) = ∑ Dxi,xi+1 + DxN,x1 i=1
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where Dj,k is the distance of the path between cities j and k. [Optional] Solutions must satisfy certain constraints.
Traveling Salesman Problem Formulation
Design variables represent a candidate solution.
• The design variable is a sequence x of N cities, where xi ∈ {1,⋯, N}, ∀i ∈ {1,⋯, N}.
The N cities to be visited are represented by values {1,…,N}.
The search space is all possible sequences of N cities, where cities are in {1,…,N}.
1, if xj = i i ∑j=1 j j 0, if xj ≠ i
∀i ∈ {1,⋯, N}, h (x) = N 1(x = i) − 1 = 0 1(x = i) =
Designing Objective Functions to Deal With Constraints
The original objective function of a problem can be modified
to deal with constraints.
A penalty can be added for infeasible solutions, increasing
their cost.
Designing Objective Functions to Deal With Constraints
1234 __ __ __ __
Objetive function:
( size(x)−1 )
E.g.: assume that the representation is a list of any size, and that
our initialisation procedure is uniformly at random with replacement.
12234 __ __ __ __ __
minimise totalDistance(x) =
+ Dxsize(x),x1
How to modify the objective function to deal with the constraint that each city must appear once and only once?
1234 __ __ __ __
Objetive function:
minimise totalDistance(x) =
where nm is the number of cities missing, nd is the number of duplications of cities and P is a large positive constant.
Designing Objective Functions to Deal With Constraints
E.g.: assume that the representation is a list of any size, and that
our initialisation procedure is uniformly at random with replacement.
12234 __ __ __ __ __
( size(x)−1 )
∑ Dxi,xi+1 + Dxsize(x),x1+nmP + ndP
Generalising The Strategy Minimise f(x)
gi(x) ≤ 0, hj(x) = 0,
{0 if x is feasible Only sum here the violated constraints P × [ga(x)2 + gb(x)2 + ⋯ + ha′(x)2 + hb′(x)2 + ⋯] otherwise where P is a large positive constant.
Subject to
i = 1,⋯,m j = 1,⋯,n
Generalising The Strategy Minimise f(x)
Subject to
gi(x) ≤ 0, hj(x) = 0,
i = 1,⋯,m j = 1,⋯,n
P × [vg1g1(x)2+vg2g2(x)2 + ⋯+vgmgm(x)2+
+vh1h1(x)2+vh2h2(x)2 + ⋯+vhnhn(x)2]
where P is a large positive constant, and vgi and vhi are 1 if the corresponding constraint is violated and 0 otherwise.
Dealing with Constraints Based on Objective Functions
Advantage:
Easier to design.
Disadvantage:
Algorithm has to search for feasible solutions.
Completeness
If we use a strategy to deal with constraints that never enables any infeasible solution to be generated, algorithms such as Hill Climbing and Simulated Annealing are complete.
Otherwise:
Hill Climbing: not complete if the objective function has local optima.
Simulated Annealing: not guaranteed to find a feasible solution within a reasonable amount of time.
We need to design strategies to deal with the constraints. Examples of strategies:
Representation, initialisation and neighbourhood operators. Objective function.
Next Example applications.
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