Improved Multicrossover Genetic Algorithm For Twodimensional Rectangular Bin Packing Problem
Bin Packing Problem is a branch of Cutting and Packing problems which has many applications in wood and metal industries. In this research we focus on non-oriented case of Two–Dimensional Rectangular Bin Packing Problem (2DRBPP). The objective of this problem is to pack a given set of small recta...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 語言: | English English |
| 出版: |
2010
|
| 主題: | |
| 在線閱讀: | http://psasir.upm.edu.my/id/eprint/11988/1/FS_2010_5_A.pdf |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | Bin Packing Problem is a branch of Cutting and Packing problems which has many
applications in wood and metal industries. In this research we focus on non-oriented
case of Two–Dimensional Rectangular Bin Packing Problem (2DRBPP). The objective
of this problem is to pack a given set of small rectangles, which may be rotated by 90˚,
without overlaps into a minimum numbers of identical large rectangles.
Our aim is to improve the performance of the MultiCrossover Genetic Algorithm
(MXGA) proposed from the literature for solving the problem. We focus on four major
components of the MXGA which consist of selection, crossover, mutation and
replacement. Initial computational experiments are conducted independently on the
named components using some benchmark problem instances. The most competitive
techniques from each component are combined to form a new algorithm called Improved MXGA (MXGAi). Extensive computational experiments are performed using
benchmark data sets to assess the effectiveness of the proposed algorithm. The MXGAi
is shown to be competitive when compared with MXGA, Standard GA, Unified Tabu
Search (UTS) and Randomised Descent Method (RDM). |
|---|