African Buffalo Optimization for One Dimensional Bin Packing Problem

2019 ◽  
Vol 10 (4) ◽  
pp. 38-52 ◽  
Author(s):  
Amira Gherboudj
Keyword(s):  
Swarm Intelligence ◽  
Bin Packing ◽  
Optimization Problems ◽  
Packing Problem ◽  
Np Hard ◽  
Computational Results ◽  
African Buffalo ◽  
One Dimensional ◽  
Bin Packing Problem ◽  
Discrete Values

African Buffalo Optimization (ABO) is one of the most recent bioinspired metaheuristics based on swarm intelligence. It is inspired by the buffalo's behavior and lifestyle. ABO Metaheuristic showed its effectiveness for solving several optimization problems. In this contribution, we present an adaptive ABO for solving the NP-hard one dimensional Bin Packing Problem (1BPP). In the proposed algorithm, we used the ABO algorithm in combination with Ranked Order Value method to obtain discrete values and Bin Packing Problem heuristics to incorporate the problem knowledge. The proposed algorithm is used to solve 1210 of 1BPP instances. The obtained results are compared with those found by recent algorithms in the literature. Computational results show the effectiveness of the proposed algorithm and its ability to achieve best and promising solutions.

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.

scholarly journals A Study of Evolutionary Algorithm Selection Hyper-Heuristics for the One-Dimensional Bin-Packing Problem

2012 ◽  
Vol 48 ◽  
Author(s):  
Nelishia Pillay
Keyword(s):  
Evolutionary Algorithm ◽  
Bin Packing ◽  
Optimization Problems ◽  
Packing Problem ◽  
Hill Climbing ◽  
Item Selection ◽  
Algorithm Selection ◽  
One Dimensional ◽  
Low Level ◽  
Bin Packing Problem

Hyper-heuristics are aimed at providing a generalized solution to optimization problems rather than producing the best result for one or more problem instances. This paper examines the use of evolutionary algorithm (EA) selection hyper-heuristics to solve the offline one-dimensional bin-packing problem. Two EA hyper-heuristics are evaluated. The first (EA-HH1) searches a heuristic space of combinations of low-level construction heuristics for bin selection. The second (EA-HH2) explores a space of combinations of both item selection and bin selection heuristic combinations. These EA hyper-heuristics use tournament selection to choose parents, and mutation and crossover with hill-climbing to create the offspring of each generation. The performance of the hyper-heuristics is compared to that of each of the low-level heuristics applied independently to solve this problem. Furthermore, the performance of both hyper-heuristics is also compared. The comparisons revealed that hyper-heuristics in general perform better than any single low-level construction heuristic in solving the problem. In addition to this it was found that the hyper-heuristic exploring a space of both item selection and bin selection heuristic combinations is more effective than the hyper-heuristic searching a space of just bin selection heuristic combinations. The performance of this hyper-heuristic was found to be comparable to other methods applied to the same benchmark sets of problems.

scholarly journals An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 97959-97974 ◽  
Author(s):  
Diaa Salama Abdul-Minaam ◽  
Wadha Mohammed Edkheel Saqar Al-Mutairi ◽  
Mohamed A. Awad ◽  
Walaa H. El-Ashmawi
Keyword(s):  
Bin Packing ◽  
Packing Problem ◽  
One Dimensional ◽  
Bin Packing Problem ◽  
The One

Solving the one-dimensional bin packing problem with a weight annealing heuristic

2008 ◽  
Vol 35 (7) ◽  
pp. 2283-2291 ◽  
Author(s):  
Kok-Hua Loh ◽  
Bruce Golden ◽  
Edward Wasil
Keyword(s):  
Bin Packing ◽  
Packing Problem ◽  
One Dimensional ◽  
Bin Packing Problem ◽  
The One

New Pretreatment and Resolution Using the Bee Colony Optimization for the Two-Dimensional Bin Packing Problem

2016 ◽  
Vol 14 ◽  
pp. 13-22
Author(s):  
Aida Kenza Amara ◽  
Bachir Djebbar
Keyword(s):  
Bin Packing ◽  
Heuristic Method ◽  
Packing Problem ◽  
Computational Results ◽  
Two Dimensional ◽  
Bee Colony ◽  
Bin Packing Problem ◽  
Bee Colony Optimization ◽  
Minimum Number

The two-dimensional bin packing problem involves packing a given set of rectangles into a minimum number of larger identical rectangles called bins. In this paper, we propose and develop mathematically a new pretreatment for the oriented version of the problem in order to reduce its size, identify and value the lost spaces by increasing the size of some objects. A heuristic method based on the first-fit strategy adapted to this problem is proposed. We present an approach of resolution using the bee colony optimization. The computational results show the effectiveness of the pretreatment in reducing the number of bins.

scholarly journals Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Marco Aurelio Sotelo-Figueroa ◽  
Héctor José Puga Soberanes ◽  
Juan Martín Carpio ◽  
Héctor J. Fraire Huacuja ◽  
Laura Cruz Reyes ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Boolean Functions ◽  
Bin Packing ◽  
Optimization Problems ◽  
Packing Problem ◽  
Grammatical Evolution ◽  
Friedman Test ◽  
Swarm Optimization ◽  
Algorithmic Problems ◽  
Test Instance

In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO.

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