A batching-move iterated local search algorithm for the bin packing problem with generalized precedence constraints

2017 ◽  
Vol 55 (21) ◽  
pp. 6288-6304 ◽  
Author(s):  
Raphael Kramer ◽  
Mauro Dell’Amico ◽  
Manuel Iori
Keyword(s):  
Local Search ◽  
Bin Packing ◽  
Search Algorithm ◽  
Packing Problem ◽  
Iterated Local Search ◽  
Precedence Constraints ◽  
Local Search Algorithm ◽  
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.

Guided Local Search for the Three-Dimensional Bin-Packing Problem

2003 ◽  
Vol 15 (3) ◽  
pp. 267-283 ◽  
Author(s):  
Oluf Faroe ◽  
David Pisinger ◽  
Martin Zachariasen
Keyword(s):  
Local Search ◽  
Bin Packing ◽  
Three Dimensional ◽  
Packing Problem ◽  
Guided Local Search ◽  
Bin Packing Problem

scholarly journals DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1429
Author(s):  
Jui-Chung Hung
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Doa Estimation ◽  
Iterated Local Search ◽  
Local Search Algorithm ◽  
Noise Variance ◽  
Swarm Optimization ◽  
Noise Effects ◽  
Low Snr ◽  
Uniform Noise

In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order to address this issue. The MPSO incorporates re-estimation of the noise variance and iterated local search algorithms into the particle swarm optimization (PSO) algorithm, resulting in higher efficiency and a reduction in non-uniform noise effects under a low SNR. The MPSO procedure is as follows: PSO is initially utilized to evaluate the signal DOA using a subspace maximum-likelihood (SML) method. Next, the best position of the swarm to estimate the noise variance is determined and the iterated local search algorithm to reduce the non-uniform noise effect is built. The proposed method uses the SML criterion to rebuild the noise variance for the iterated local search algorithm, in order to reduce non-uniform noise effects. Simulation experiments confirm that the DOA estimation methods are valid in a high SNR environment, but in a low SNR and non-uniform noise environment, the performance becomes poor because of the confusion between noise and signal sources. The proposed method incorporates the re-estimation of noise variance and an iterated local search algorithm in the PSO. This method is effectively improved by the ability to reduce estimation deviation in low SNR and non-uniform environments.

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