COMP2051代做、Algorithm代写、代写C/C++、代做C/C++程序设计

日期：2020-04-22 10:42

Artificial Intelligence Methods (COMP2051 or AE2AIM) Coursework 1

Artificial Intelligence Methods (COMP2051 or AE2AIM)

Coursework: Memetic Algorithm for Multi-Dimensional Knapsack Problem

1. Introduction

Multi-dimensional knapsack problem is a classic NP-Hard combinatorial optimisition

problem used to test the performance metaheuristics. In this coursework, you are asked to

write a C/C++ program to solve this problem using a memetic algorithm (a variant of

genetic algorithm). In addition to submitting source code, a report (no more than 2000 words

and 6 pages) is required to describe the algorithm, the experimental results, discussions and

reflections on results and performance of the algorithm. This coursework carries 50% of

the module marks. The rest of module marks comes from the final written exam.

2. Multi-dimensional Knapsack Problem

Multi-dimensional knapsack problem is an extension of the 1D knapsack problem by adding

capacity constraints in multiple dimensions. The problem can be formally defined as follows.

Given a set of n items numbered from 1 up to n, each with a size vector v=(v1j, v2j, v3j, …, vmj)

where vij is the i-th dimensional size of item j. bi is the i-th dimensional size of knapsack. pj

is the profit of item j if it is included in the knapsack. Denote xj be the binary variables to

indicate whether item j is included in the knapsack (=1) or not (=0). The problem to be solved

is then formulated as follows

Artificial Intelligence Methods (COMP2051 or AE2AIM) Coursework 2

3. Problem instances

In this coursework, you are asked to attempt some more challenging instances from paper:

P.C. Chu and J.E.Beasley "A genetic algorithm for the multidimensional knapsack problem",

Journal of Heuristics, vol. 4, 1998, pp63-86. All the data files are compressed in mknapinstances.zip,

downloadable from Moodle. The zip file includes 9 problem instance

files (each containing 30 instances), 1 data format file file-format.txt and 1best

known solution file best-feasible-slns.txt.

4. Experiments conditions and requirements

The following requirements should be satisfied by your program:

(1) You are required to submit two files only. The first file should contain all your

program source codes. The second file is a coursework report.

(2) Your source code should be properly commented.

(3) Your report should include the details of your algorithm (pseudo-code), a description

of the parameter tuning process, the results that your algorithm obtains in comparison

with the best results in the literature (i.e. gap% to the best results), a short

reflection/discussion on the strengths and weaknesses of GA/MA methods.

(4) Name your program file after your student id. For example, if your student number is

2019560, name your program as 2019560.c (or 2019560.cpp).

(5) Your program should compile without errors on CSLinux Server. Therefore, please

fully tested before submission. You may use one of the following commands

(assuming your student id is 2019560 and your program is named after your id):

gcc -std=c99 2019560.c -o 2019560

or

g++ -std=c++11 2019560.cpp -o 2019560

(6) After compilation, your program should be executable using the following command:

./2019560 -s data_file -o solution_file -t max_time

where 2019560 is the executable file of your program, data_file is one of

problem instance files specified in Section 3. max_time is the maximum time

permitted for a single run of your MA algorithm. soluton_file is the file for

output the best solutions by your MA algorithm. The format should be as follows:

# of problems

objective value of instance 1

x1 x2 x3 … x_n

Artificial Intelligence Methods (COMP2051 or AE2AIM) Coursework 3

objective value of instance 2

x1 x2 x3 … x_n

… …

objective value of last instance

x1 x2 x3 … x_n

An example solution file for problem data file “mknapcb1.txt” is available on

moodle.

(7) The solution file that your algorithm (solution_file) is expected to pass a

solution checking test successfully using the following command:

./mk_checker -s problem_file -c solution_file

where problem_file is one of problem data files in Section 3. If your solution file

format is correct, you should get the following command line message “All solutions

are feasible with correct objective values.”

The solution checker can be downloaded from moodle page. The checker is runnable

on CS linux server only.

(8) Your MA algorithm should run once only for each problem instance and each run

should take no more than 5 min (i.e. 5 min max_time is set as your stopping criteria of

your MA algorithm).

(9) Please carefully check the memory management in your program and test your

algorithm with a full run on CSLinux (i.e. running 5 instances in one go). In past,

some submitted programs can run for 1-2 instances but then crashed because of outof-memory

error. This, if happens, will greatly affect your score.

5. Marking criteria

• The quality of the experimental results (30%). Your algorithm shall be tested for a file

containing 5 instances with sizes ranging from 100 items-5 dimensions to 250 items-

30 dimensions. Only those solutions that are very close or exceeding the best know

solutions will get full mark.

• The quality of codes (30%)

• Report (40%)

6. Submission deadline

28th April 2019, 4pm Beijing Time

Artificial Intelligence Methods (COMP2051 or AE2AIM) Coursework 4

7. How to submit

To be conformed in due course.