Building Hyper-heuristics Through Ant Colony Optimization for the 2D Bin Packing Problem

2005 ◽  
pp. 654-660 ◽  
Author(s):  
Alberto Cuesta-Cañada ◽  
Leonardo Garrido ◽  
Hugo Terashima-Marín
Keyword(s):  
Ant Colony Optimization ◽  
Bin Packing ◽  
Packing Problem ◽  
Ant Colony ◽  
Bin Packing Problem

Optimization by Ant Colony Hybrid Local Search for Online Class Constrained Bin Packing Problem

2013 ◽  
Vol 311 ◽  
pp. 123-128 ◽  
Author(s):  
Tsai Duan Lin ◽  
Chiun Chieh Hsu ◽  
Li Fu Hsu
Keyword(s):  
Local Search ◽  
Bin Packing ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Packing Problem ◽  
Ant Colony ◽  
Np Hard ◽  
Combinatorial Optimization Problems ◽  
Bin Packing Problem

The on-line Class Constrained Bin Packing problem (CCBP) is one of variant version of the Bin Packing Problem (BPP). The BPP is to find the minimum numbers of bins needed to pack a given set of items of known sizes so that they do not exceed the capacity B of each bin. In the CCBP, we are given bins of capacity B with C compartments and n items of Q different classes, each item i is belong to 1,2,…,n with class qi and si. The CCBP is to pack the items into bins, where each bin contains at most Q different classes and has total items size at most B. This CCBP is known to be NP-hard combinatorial optimization problems. In this paper, we used an ant colony optimization (ACO) approach with a simple but very effective local search algorithm to resolve this NP-hard problem. After the experimental design, limited computational results show the efficiency of this scheme. It is also shown that the ACO approach can outperform some existing methods, whereas the hybrid approach can compete with the known solution methods.

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