A New Effective Hybrid Optimization Strategy for Rectangular Packing Problem

Yu Yu Zhou; Yun Qing Rao; Chao Yong Zhang; Guo Jun Zhang

doi:10.4028/www.scientific.net/AMR.189-193.3131

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 189-193A New Effective Hybrid Optimization Strategy for...

A New Effective Hybrid Optimization Strategy for Rectangular Packing Problem

Abstract:

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.

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Periodical:

Advanced Materials Research (Volumes 189-193)

Pages:

3131-3136

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.189-193.3131

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Online since:

February 2011

Authors:

Yu Yu Zhou, Yun Qing Rao, Chao Yong Zhang, Guo Jun Zhang

Keywords:

Genetic Algorithm (GA), Hybrid, Rectangular Packing Problem, Simulated Annealing (SA)

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