scholarly journals An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem

2021 ◽  
Vol 11 (1) ◽  
pp. 413
Author(s):  
Yi-Bo Li ◽  
Hong-Bao Sang ◽  
Xiang Xiong ◽  
Yu-Rou Li
Keyword(s):  
Genetic Algorithm ◽  
Packing Problem ◽  
Global Perspective ◽  
Adaptive Genetic Algorithm ◽  
Two Dimensional ◽  
Filling Rate ◽  
Zero Waste ◽  
Population Evolution ◽  
Rectangular Packing ◽  
The Impact

This paper proposes the hybrid adaptive genetic algorithm (HAGA) as an improved method for solving the NP-hard two-dimensional rectangular packing problem to maximize the filling rate of a rectangular sheet. The packing sequence and rotation state are encoded in a two-stage approach, and the initial population is constructed from random generation by a combination of sorting rules. After using the sort-based method as an improved selection operator for the hybrid adaptive genetic algorithm, the crossover probability and mutation probability are adjusted adaptively according to the joint action of individual fitness from the local perspective and the global perspective of population evolution. The approach not only can obtain differential performance for individuals but also deals with the impact of dynamic changes on population evolution to quickly find a further improved solution. The heuristic placement algorithm decodes the rectangular packing sequence and addresses the two-dimensional rectangular packing problem through continuous iterative optimization. The computational results of a wide range of benchmark instances from zero-waste to non-zero-waste problems show that the HAGA outperforms those of two adaptive genetic algorithms from the related literature. Compared with some recent algorithms, this algorithm, which can be increased by up to 1.6604% for the average filling rate, has great significance for improving the quality of work in fields such as packing and cutting.

An Adaptive Genetic Algorithm Based on Multi-Population Parallel Evolutionary for Highway Alignment Optimization Model

2011 ◽  
Vol 58-60 ◽  
pp. 1499-1503 ◽  
Author(s):  
Jian Xin Chen ◽  
Yong Yi Guo ◽  
Mai Xia Lv
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Single Species ◽  
Adaptive Genetic Algorithm ◽  
Parallel Genetic Algorithm ◽  
Single Population ◽  
Highway Design ◽  
Population Evolution ◽  
Highway Alignment Optimization ◽  
Highway Alignment

Based on the characteristics of the highway design, this paper transfers all the factors involved in the highway design to a cost-optimized-oriented model and designs a variety parallel genetic algorithm to optimize highway design. While maintaining evolution stability of excellent individual, the algorithm can improve convergence rate and accuracy and avoid premature convergence generated by single-population evolution. To some extent, it makes up generalization-lacking defects of a single species or steady parameters in premature overcoming. Finally, the algorithm is verified with a good result. This algorithm provides a useful method for highway design.

THE TWO-DIMENSIONAL PACKING PROBLEM FOR IRREGULAR OBJECTS

2004 ◽  
Vol 13 (03) ◽  
pp. 429-448 ◽  
Author(s):  
PING CHEN ◽  
ZHAOHUI FU ◽  
ANDREW LIM ◽  
BRIAN RODRIGUES
Keyword(s):  
Genetic Algorithms ◽  
Basic Problem ◽  
Piecewise Linear ◽  
Three Dimensional ◽  
Packing Problem ◽  
Two Dimensional ◽  
Linear Representations ◽  
Rectangular Packing ◽  
Work Done ◽  
Cutting Problems

Packing and cutting problems arise in a wide variety of industrial situations. The basic problem is that of determining a good arrangement of objects in a region without any overlap. Much research has been done on two and three dimensional rectangular packing while there has been little work done on irregular packing. In this work, we study the two-dimensional irregular packing problem and provide heuristic solutions which use rectilinear and piecewise-linear representations of objects. These heuristics include Genetic Algorithms and Tabu Search. Experimentation gives good results.

Hybrid Genetic Algorithm with Simulated Annealing Based on Best-Fit Strategy for Rectangular Packing Problem

2010 ◽  
Vol 118-120 ◽  
pp. 379-383
Author(s):  
Yu Yu Zhou ◽  
Yun Qing Rao ◽  
Chao Yong Zhang ◽  
Liang Gao
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Packing Problem ◽  
Crossover Operator ◽  
Complete Problems ◽  
Np Complete ◽  
Rectangular Packing ◽  
Best Fit

In this paper we address a rectangular packing problem (RPP), which is one of the most difficult NP-complete problems. Borrowing from the respective advantages of the two algorithms, a hybrid of genetic algorithm (GA) and simulated annealing (SA) is developed to solve the RPP. Firstly, we adopt and improve Burke’s best-fit (BF) placement strategy, which is not restricted to the first shape but may search the list for better candidate shapes for placement. Secondly, we propose a new crossover operator, named Improved Precedence Operation Crossover (IPOX), which can preserve the valuable characteristics of the previous generation. At last, using a new temperature and iterations strategy and Boltzmann-type operator, we propose SA to re-intensify search from the promising solutions. The computational results validate the quality and the effectiveness of hybrid algorithm.

Sign in / Sign up

Export Citation Format

Share Document