Hybrid genetic algorithm and simulated annealing for two-dimensional non-guillotine rectangular packing problems

2006 ◽  
Vol 19 (5) ◽  
pp. 557-567 ◽  
Author(s):  
Alev Soke ◽  
Zafer Bingul
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Genetic Algorithm ◽  
Two Dimensional ◽  
Packing Problems ◽  
Rectangular Packing

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.

Two-dimensional nesting system based on hybrid genetic algorithm

2009 ◽  
Vol 14 (1) ◽  
pp. 60-64
Author(s):  
Qingming Wu ◽  
Wei Yang ◽  
Qiang Zhang ◽  
Junjie Zhou
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Two Dimensional

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

A Hybrid Genetic Algorithm for Optimization of Two-Dimensional Cutting-Stock Problem

2012 ◽  
pp. 56-71
Author(s):  
Ahmed Mellouli ◽  
Faouzi Masmoudi ◽  
Imed Kacem ◽  
Mohamed Haddar
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Production Cost ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Genetic Approach ◽  
Cutting Pattern

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfill the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.

Multilevel redundancy allocation using two dimensional arrays encoding and hybrid genetic algorithm

2013 ◽  
Vol 64 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Pan He ◽  
Kaigui Wu ◽  
Jie Xu ◽  
Junhao Wen ◽  
Zhuo Jiang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Two Dimensional ◽  
Redundancy Allocation
Sign in / Sign up

Export Citation Format

Share Document