A New Effective Hybrid Optimization Strategy for Rectangular Packing Problem

2011 ◽  
Vol 189-193 ◽  
pp. 3131-3136
Author(s):  
Yu Yu Zhou ◽  
Yun Qing Rao ◽  
Chao Yong Zhang ◽  
Guo Jun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Packing Problem ◽  
Hybrid Optimization ◽  
Optimization Strategy ◽  
Complete Problems ◽  
Np Complete ◽  
Rectangular Packing

In this paper we address a rectangular packing problem (RPP), which is one of the most difficult NP-complete problems. First, greedy biggest space sequencing (GBSS) is presented as a new placement strategy, which is very essential to RPP. Then, borrowing from the respective advantages of the two algorithms, genetic algorithm (GA) and simulated annealing (SA), a hybrid optimization policy is developed. The hybrid GASA is subjected to a test using a set of benchmarks. Compared to other approaches from the literature the hybrid optimization strategy performs better.

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.

Priority-based task scheduling on heterogeneous resources in the Expert Cloud

Kybernetes ◽  
2015 ◽  
Vol 44 (10) ◽  
pp. 1455-1471 ◽  
Author(s):  
Mehran Ashouraie ◽  
Nima Jafari Navimipour
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Design Methodology ◽  
Fitness Function ◽  
Content Type ◽  
Cloud Systems ◽  
New Class ◽  
Complete Problems ◽  
Np Complete ◽  
Real World Situation

Purpose – Expert Cloud as a new class of Cloud systems provides the knowledge and skills of human resources (HRs) as a service using Cloud concepts. Task scheduling in the Expert Cloud is a vital part that assigns tasks to suitable resources for execution. The purpose of this paper is to propose a method based on genetic algorithm to consider the priority of arriving tasks and the heterogeneity of HRs. Also, to simulate a real world situation, the authors consider the human-based features of resources like trust, reputation and etc. Design/methodology/approach – As it is NP-Complete to schedule tasks to obtain the minimum makespan and the success of genetic algorithm in optimization and NP-Complete problems, the authors used a genetic algorithm to schedule the tasks on HRs in the Expert Cloud. In this method, chromosome or candidate solutions are represented by a vector; fitness function is calculated based on several factors; one point cross-over and swap mutation are also used. Findings – The obtained results demonstrated the efficiency of the proposed algorithm in terms of time complexity, task fail rate and HRs utilization. Originality/value – In this paper the task scheduling issue in the Expert Cloud and improving pervious algorithm are pointed out and the approach to resolve the problem is applied into a practical example.

Solving the Independent-set Problem in a DNA-Based Supercomputer Model

2005 ◽  
Vol 15 (04) ◽  
pp. 469-479 ◽  
Author(s):  
WENG-LONG CHANG ◽  
MINYI GUO ◽  
JESSE WU
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Deoxyribonucleic Acid ◽  
Independent Set ◽  
Parallel Genetic Algorithm ◽  
Independent Set Problem ◽  
Complete Problems ◽  
Np Complete

In this paper, it is demonstrated how the DNA (DeoxyriboNucleic Acid) operations presented by Adleman and Lipton can be used to develop the parallel genetic algorithm that solves the independent-set problem. The advantage of the genetic algorithm is the huge parallelism inherent in DNA based computing. Furthermore, this work represents obvious evidence for the ability of DNA based parallel computing to solve NP-complete problems.

405 Hybrid Optimization Method Using Simulated Annealing and Genetic Algorithm

2009 ◽  
Vol 2009 (0) ◽  
pp. _405-1_-_405-6_
Author(s):  
Yuichiro SAKAMOTO ◽  
Yasuhiro BONKOBARA ◽  
Takahiro KONDOU ◽  
Hiroyuki KUROKI ◽  
Yuki SAKAI
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Method ◽  
Hybrid Optimization ◽  
Hybrid Optimization Method

Convergence and finite-time behavior of simulated annealing

1986 ◽  
Vol 18 (03) ◽  
pp. 747-771 ◽  
Author(s):  
Debasis Mitra ◽  
Fabio Romeo ◽  
Alberto Sangiovanni-Vincentelli
Keyword(s):  
Markov Chain ◽  
Simulated Annealing ◽  
Finite Time ◽  
Randomized Algorithm ◽  
Global Optimum ◽  
Time Behavior ◽  
Annealing Schedule ◽  
Complete Problems ◽  
Inhomogeneous Markov Chain ◽  
Np Complete

Simulated annealing is a randomized algorithm which has been proposed for finding globally optimum least-cost configurations in large NP-complete problems with cost functions which may have many local minima. A theoretical analysis of simulated annealing based on its precise model, a time-inhomogeneous Markov chain, is presented. An annealing schedule is given for which the Markov chain is strongly ergodic and the algorithm converges to a global optimum. The finite-time behavior of simulated annealing is also analyzed and a bound obtained on the departure of the probability distribution of the state at finite time from the optimum. This bound gives an estimate of the rate of convergence and insights into the conditions on the annealing schedule which gives optimum performance.

The Application of DNA Self-Assembly Model for Bin Packing Problem

2012 ◽  
Vol 3 (1) ◽  
pp. 1-15
Author(s):  
Yanfeng Wang ◽  
Xuewen Bai ◽  
Donghui Wei ◽  
Weili Lu ◽  
Guangzhao Cui
Keyword(s):  
Self Assembly ◽  
Bin Packing ◽  
Formal Model ◽  
Linear Time ◽  
Packing Problem ◽  
Assembly Model ◽  
Dna Tiles ◽  
Bin Packing Problem ◽  
Complete Problems ◽  
Np Complete

Bin Packing Problem (BPP) is a classical combinatorial optimization problem of graph theory, which has been proved to be NP-complete, and has high computational complexity. DNA self-assembly, a formal model of crystal growth, has been proposed as a mechanism for the bottom-up fabrication of autonomous DNA computing. In this paper, the authors propose a DNA self-assembly model for solving the BPP, this model consists of two units: grouping based on binary method and subtraction system. The great advantage of the model is that the number of DNA tile types used in the model is constant and it can solve any BPP within linear time. This work demonstrates the ability of DNA tiles to solve other NP-complete problems in the future.

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.

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