scholarly journals A Hybrid Heuristic Algorithm for the Rectangular Packing Problem

2005 ◽  
pp. 783-791 ◽  
Author(s):  
Defu Zhang ◽  
Ansheng Deng ◽  
Yan Kang
Keyword(s):  
Heuristic Algorithm ◽  
Packing Problem ◽  
Hybrid Heuristic ◽  
Hybrid Heuristic Algorithm ◽  
Rectangular Packing

A hybrid heuristic algorithm for the 2D variable-sized bin packing problem

2014 ◽  
Vol 238 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Shaohui Hong ◽  
Defu Zhang ◽  
Hoong Chuin Lau ◽  
XiangXiang Zeng ◽  
Yain-Whar Si
Keyword(s):  
Heuristic Algorithm ◽  
Bin Packing ◽  
Packing Problem ◽  
Hybrid Heuristic ◽  
Bin Packing Problem ◽  
Hybrid Heuristic Algorithm

scholarly journals Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 219
Author(s):  
Xiang Tian ◽  
Xiyu Liu
Keyword(s):  
Local Search ◽  
Heuristic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Membrane System ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Hybrid Heuristic ◽  
Shop Scheduling ◽  
Hybrid Heuristic Algorithm

In real industrial engineering, job shop scheduling problem (JSSP) is considered to be one of the most difficult and tricky non-deterministic polynomial-time (NP)-hard problems. This study proposes a new hybrid heuristic algorithm for solving JSSP inspired by the tissue-like membrane system. The framework of the proposed algorithm incorporates improved genetic algorithms (GA), modified rumor particle swarm optimization (PSO), and fine-grained local search methods (LSM). To effectively alleviate the premature convergence of GA, the improved GA uses adaptive crossover and mutation probabilities. Taking into account the improvement of the diversity of the population, the rumor PSO is discretized to interactively optimize the population. In addition, a local search operator incorporating critical path recognition is designed to enhance the local search ability of the population. Experiment with 24 benchmark instances show that the proposed algorithm outperforms other latest comparative algorithms, and hybrid optimization strategies that complement each other in performance can better break through the original limitations of the single meta-heuristic method.

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