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.

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.

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

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.

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