Cooperative parallel grouping genetic algorithm for the one-dimensional bin packing problem

Küçükyılmaz, Tayfun; Kızılöz, Hakan Ezgi

doi:10.1016/j.cie.2018.08.021

Cited by 36 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The packing problem can be divided into three categories according to spatial dimensions and application types: the one-dimensional (1D) packing problem (wire packing problem) (Loh et al, 2008;Fleszar and Charalambous, 2011;Kucukyilmaz and Kiziloz, 2018), as shown in Figure 1; the 2D packing problem (planar packing problem); and the three-dimensional (3D) packing problem (Martello et al, 2000;Allen et al, 2011;Paquay et al, 2018;Lamas-Fernandez et al, 2022;Zhao et al, 2022), as shown in Figure 2. According to the regularity of shapes of participating parts, the 2D packing problem can be further divided into 2D regular (such as Figure 3) and irregular packing problems.…”

Section: Packing Problem Classificationmentioning

confidence: 99%

Two-dimensional irregular packing problems: A review

Guo

et al. 2022

Front. Mech. Eng.

View full text Add to dashboard Cite

Two-dimensional (2D) irregular packing problems are widespread in manufacturing industries such as shipbuilding, metalworking, automotive production, aerospace, clothing and furniture manufacturing. Research on 2D irregular packing problems is essential for improving material utilization and industrial automation. Much research has been conducted on this problem with significant research results and certain algorithms. The work has made important contributions to solving practical problems. This paper reviews recent advances in the domain of 2D irregular packing problems based on a variety of research papers. We first introduce the basic concept and research background of 2D irregular packing problems and then summarize algorithms and strategies that have been proposed for the problems in recent years. Conclusion summarize development trends and research hotspots of typical 2D irregular shape packing problems. We hope that this review could provide guidance for researchers in the field of 2D irregular packing.

show abstract

Section: Packing Problem Classificationmentioning

confidence: 99%

Two-dimensional irregular packing problems: A review

Guo

et al. 2022

Front. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…In GGA-CGT (grouping genetic algorithm with controlled gene transmission) [44], the transmission of the best genes in the chromosomes was promoted while keeping the balance between selection pressure and population diversity. Kucukyilmaz and Kiziloz proposed island-parallel GGA (IPGGA) in [45]. It realized the choice of communication topology, determined the migration and assimilation strategies, adjusted the migration rate, and exploited diversification technologies.…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

A Flexible Reinforced Bin Packing Framework with Automatic Slack Selection

Yang

Luo

Fuentes

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The slack-based algorithms are popular bin-focus heuristics for the bin packing problem (BPP). The selection of slacks in existing methods only consider predetermined policies, ignoring the dynamic exploration of the global data structure, which leads to nonfully utilization of the information in the data space. In this paper, we propose a novel slack-based flexible bin packing framework called reinforced bin packing framework (RBF) for the one-dimensional BPP. RBF considers the RL-system, the instance-eigenvalue mapping process, and the reinforced-MBS strategy simultaneously. In our work, the slack is generated with a reinforcement learning strategy, in which the performance-driven rewards are used to capture the intuition of learning the current state of the container space, the action is the choice of the packing container, and the state is the remaining capacity after packing. During the construction of the slack, an instance-eigenvalue mapping process is designed and utilized to generate the representative and classified validate set. Furthermore, the provision of the slack coefficient is integrated into MBS-based packing process. Experimental results show that, in comparison with fit algorithms, MBS and MBS’, RBF achieves state-of-the-art performance on BINDATA and SCH_WAE datasets. In particular, it outperforms its baseline MBS and MBS’, averaging the number increase of optimal solutions of 189.05% and 27.41%, respectively.

show abstract

“…Based on the advantages of metaheuristic algorithms over exact methods, one of the most popular metaheuristic algorithms, GA, has been extensively applied to solve the BPP [25], [26]. In [27], a new island parallel grouping GA (IPGG) was developed to solve the 1D-BPP based on a modified class of the GA designed for solving complex grouping problems. Ross et al [28], [29] solved the BPP through combinations of GAs and hyperheuristics.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

In recent years, the one-dimensional bin packing problem (1D-BPP) has become one of the most famous combinatorial optimization problems. The 1D-BPP is a robust NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swarm algorithm and fitness-dependent optimizer (FDO), named the AFDO, to solve the BPP. The proposed algorithm is based on the generation of a feasible initial population through a modified well-known first fit (FF) heuristic approach. To obtain a final optimized solution, the most critical parameters of the algorithm are adapted for the problem. To the best of our knowledge, this is the first study to apply the FDO algorithm in a discrete optimization problem, especially for solving the BPP. The adaptive algorithm was tested on 30 instances obtained from benchmark datasets. The performance and evaluation results of this algorithm were compared with those of other popular algorithms, such as the particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), and Jaya algorithm. The AFDO algorithm obtained the smallest fitness values and outperformed the PSO, CS, and Jaya algorithms by 16%, 17%, and 11%, respectively. Moreover, the AFDO shows superiority in terms of execution time with improvements over the execution times of the PSO, CS, and Jaya algorithms by up to 46%, 54%, and 43%, respectively. The experimental results illustrate the effectiveness of the proposed adaptive algorithm for solving the 1D-BPP. INDEX TERMS Bin packing, first fit heuristic, fitness-dependent optimizer, swarm intelligent algorithms.

show abstract

Cooperative parallel grouping genetic algorithm for the one-dimensional bin packing problem

Cited by 36 publications

References 36 publications

Two-dimensional irregular packing problems: A review

Two-dimensional irregular packing problems: A review

A Flexible Reinforced Bin Packing Framework with Automatic Slack Selection

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

Contact Info

Product

Resources

About