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BTH004留学生作业代做、代写algorithm课程作业、代写java, c/c++，python程序语言作业
代做R语言编程|代写留学生 Statistic

BTH004 - Laboratory assignment 1

In this laboratory assignment you should design and implement algorithms

for the multiple knapsack problem. The assignment contains two parts; one is

mandatory and one is optional.

In part 1 (the mandatory part) you should design and implement two algorithms

for the multiple knapsack problem:

1. A (contructive) greedy algorithm.

2. An improving search (neighborhood search) algorithm.

Part 2 (the optional part) concerns implementing a tabu-search algorithm

(a meta-heuristic) for the considered problem.

The assignment should be conducted individually or in groups of two students,

and it will be examined through a written report (one per group). The

report should fulfill the requirements specified below.

Note that you are allowed to use any high-level programming language you

find suitable, e.g., java, c, c++, and python.

It is highly recommended that you start working with the assignment as soon

as possible. It is of particular importance that you start before the scheduled

supervision time, so that you can make as much use as possible of the teacher

support.

The knapsack problem

The (standard) knapsack problem, which was introduced earlier in the course,

can be described in the following way. Assume that you have a set of items, each

with a (positive) weight and a (positive) value, and a knapsack (or bag) with a

limited weight capacity. The problem is to choose (to include in the knapsack)

items so that the weight capacity of the knapsack is not violated and the value

of the chosen items are as high as possible.

Mathematically, the problem can be formulated in the following way. We let

I = {1, . . . , n} be an index set over the n items, where item i ∈ I have a value

pi > 0 and a weight wi > 0, and we let W > 0 denote the weight capacity of the

knapsack. For item i ∈ I, the binary decision variable xi

is used to determine

whether to include item i in the knapsack: xi = 1 if the item is chosen, and

xi = 0 if it is not chosen.

The objective function of the problem is to maximize the utility of the chosen

items, i.e.,

All valid solutions to the problem should fulfill the weight constraint

X

i∈I

wixi ≤ W,

and the values of the decision variables are restricted by the constraint set

xi ∈ {0, 1} ∀i ∈ I.

The multiple knapsack problem

The multiple knapsack problem is an extension to the standard knapsack problem

in that it considers choosing items to include in m knapsacks (the standard

knapsack problem considers only one knapsack).

Mathematically, the multiple knapsack problem can be formulated as follows.

We let I = {1, . . . , n} be an index set over the n items, where item i ∈ I have

a value pi > 0 and a weight wi > 0. In addition, we let J = {1, . . . , m} be an

index set over the m knapsacks, where Wj > 0 denotes the weight capacity of

knapsack j ∈ J. For item i ∈ I and knapsack j ∈ J, we let the binary decision

variable xij determine whether to include item i in knapsack j: xij = 1 if item

i is included in knapsack j, otherwise xij = 0.

The objective function of the problem is to maximize the utility of the chosen

items, i.e.,

For each of the knapsacks, the solution space is restricted by a weight capacity

constraint, which states that the total weight of the selected items for that

knapsack is not allowed to exceed the weight capacity of the knapsack. This

is modeled by the following constraint set (one constraint for each of the m

knapsacks):

In addition, it needs to be explicitly modeled that an item is not allowed to be

included in more than one of the knapsacks. This is modeled by the following

constraint set (one constraint for each of the n items):

X

j∈J

xij ≤ 1, i ∈ I.

Finally, the values of the decision variables are restricted by the constraint set

xij ∈ {0, 1}, i ∈ I, j ∈ J.

Part 1 - Greedy and neighborhood search algorithms

In the first part (the mandatory part), you should design and implement 1)

a greedy algorithm and 2) a neighborhood search algorithm for the multiple

knapsack problem.

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Greedy algorithm

As discussed earlier in the course, a greedy algorithm is an algorithm that starts

with an empty solution, and iteratively adds solution components to partial

solution until a complete solution is found.

For knapsack problems, it is possible to construct greedy algorithms that are

based on the relative benefit per weight unit for the considered items.

denote the relative benefit per weight unit for item i ∈ I. By comparing

for two items i′, i′′ ∈ I, it is possible to make a greedy

decision on which item to include.

A greedy algorithm for the multiple knapsack problem could be designed by

iteratively adding to some knapsack the item with highest relative benefit (i.e.,i′ = arg maxi∈I

(bi), such that i′ has not already been included in an earlier iteration).

Please note that there are algorithm details not discussed above, which you

need to define yourself1

. For example, termination criteria and choice of knapsack

in case there is sufficient amount of capacity in more than one of the

knapsacks, has not been discussed above. In addition, you need to consider

what you should do if there is no capacity for the item with highest relative

value, but there are other items with smaller weight that can be added.

Improving search (neighborhood search) algorithm

Improving search heuristics are algorithms that iteratively improve a feasible

solution by doing small modifications of the most recent solution.

For a solution x, we define a neighborhood N(x) as all solutions “close

enough” to x (according to some criteria). If y ∈ N(x), then y is a neighbor of

x.

Neighborhood search algorithms are improving search algorithms where a

current solution is iteratively updated by choosing the “best” solution in the

neighborhood of the current solution. An algorithm description (for a maximization

problem) is provided below.

Step 0: Find a feasible (starting) solution x

(0) with value (cost) c(x

(0)) and set

solution index t = 0.

Step 1: Determine (all points in) neighborhood N(x(t)).

Step 2: If c(x(t)) ≥ c(x) ∀ x ∈ N(x(t)), then break with local optimal solution

x(t).

Set t = t + 1 and goto step 1.

As starting solution to the neighborhood search algorithm, you could, for

example, use the solution obtained by your greedy algorithm.

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It is typically a good idea to test different approaches to see which performs best

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A challenge is how to determine an appropriate neighborhood for the multiple

knapsack problem. If the neighborhood is too large, it will be impossible to

iterate over all solutions in the neighborhood, if it is too small, there is a high

risk that the algorithm will get stuck in a local optima that is not so good.

A possible way to define a neighborhood is to rotate items. As long as you

have at least one item that is not included in any knapsack, you could define the

neighborhood of a solution as all other feasible solutions that can be obtained

by:

• Moving some item (i

′

) from one knapsack (m′

) to another knapsack (m′′).

• Moving some other item (i

′′) away from knapsack (m′′) (in order to make

room for item i

′

). In the obtained solution, i

′′ is not included in any of

the knapsacks.

• Moving some other item (i

′′′), which is not included in any knapsack in

the current solution, into knapsack m′

.

If bi

′′′ > bi

′′ a better solution has been found.

When using this type of neighborhood, it is important to consider that it

might be possible to, via rotations, make room for an additional item in some

of the knapsacks after some item has been replaced by some other item. This

needs to be explicitly included in the neighborhood definition.

You are encouraged to identify some other neighborhood that you find appropriate

for the considered problem. For example, you might find it interesting

to consider rotation involving more than 3 items; however you need to consider

that this increases the search space significantly.

Part 2 - Tabu-search

A problem with the neighborhood search heuristics is that they eventually will

get stuck in a locally optimal solution. A locally optimal solution is a solution

that cannot be improved by moving to any of the solutions that are sufficiently

close in the search space (i.e., in the neighborhood), but there are solutions

farther away that are better.

The ideas of tabu-search methods, which are based on neighborhood search,

are:

• It is possible to move to a solution with worse objective if there is no

improving solution in the neighborhood (note that it is necessary to keep

track of the so far best found solution).

• The k most recent solutions are tabu, i.e., they cannot be visited (otherwise

the algorithm might immediately go back to the locally optimal

solution).

Tabu search algorithms (for maximization problems) can be described in a

general way using the following algorithm description.

Step 0: Find a feasible (starting) solution x

(0) with value (cost) c(x

(0)) and set

solution index t = 0.

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Step 1: Determine breaking criteria. For instance, break if maximum number

of iterations has been reached, or no non-tabu solutions exists.

Step 2: Determine neighborhood N(x

(t)

).

Step 3: Choose x

(t+1) ∈ N(x

(t)

) which is not in the tabu-list, and which maximizes

c(x).

Step 4: Update tabu-list (remove the oldest solution in the tabu-list and add

the most recent solution, i.e., x

(t)

), set t = t + 1 and go to step 1.

In part 2 (the optional part) you are encouraged to extend your neighborhood

search algorithm into a tabu-search algorithm.

Requirements on report

The report should contain (at least):

1. Names of all group members.

2. Descriptions of the designed and implemented algorithms, including design

choices, such as neighborhood definition, tabu list length, termination

criteria, etc.

3. Pseudo code for each of the developed algorithms.

4. Description of how you “showed” the correctness of your algorithms; in

particular you need to describe which test cases you used when testing

your algorithms.

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