African Buffalo Optimization for One Dimensional Bin Packing Problem

African Buffalo Optimization for One Dimensional Bin Packing Problem

Amira Gherboudj (MISC laboratory, Department of Computer Science and its Applications Abdelhamid Mehri University, Constantine, Algeria)
Copyright: © 2019 |Pages: 15
DOI: 10.4018/IJSIR.2019100103

Abstract

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.
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2. Bin Packing Problem

Bin Packing Problems are combinatorial grouping problems. They can be classified based on their dimension into: one dimensional, two dimensional, three dimensional and multi-dimensional Bin Packing problems. BPP appear in large practical optimization problems in real life and industrial applications including: logistics, manufacturing, wireless communication and engineering (Garey & Johnson, 1979; Fleszar, 2012; Sathe et al., 2009; Dokeroglu & Cosar, 2014). They also occur as main or sub problem in several optimization problems such as Cutting Stock problem (Gilmore & Gomory, 1961, 1963), Scheduling problems (Davidovié et al., 2012; Qiao et al., 2010), Loading and Resource Allocation problem (Zezzatti et al., 2012), Sheet Metal Forming Processes (Sathe et al., 2009) and problems of the Cloud Computing Management (Régin & Rezgui, 2011).

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