Optimum Assembly Sequence Planning System Using Discrete Artificial Bee Colony Algorithm

Özmen, Özkan; Batbat, Turgay; Özen, Tolgan; Sınanoğlu, Cem; Güven, Ayşegül

doi:10.1155/2018/3407646

Cited by 22 publications

(10 citation statements)

References 25 publications

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“…It needs to be solved by Discrete Artificial Bee Colony (DABC) algorithm. At present, the DABC algorithm has shown great performance in the combination optimization problem of dynamic deployment, traveling salesman problem, flow shop scheduling problem, optimum assembly sequence planning etc [25]- [28]. Therefore, we choose the DABC algorithm to solve the model.…”

Section: Solving Methods Based On Discrete Artificial Bee Colony Algorithmmentioning

confidence: 99%

Repair Strategy of Military Communication Network Based on Discrete Artificial Bee Colony Algorithm

2020

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After the military communications network is attacked by the enemy, in order to ensure that the information flow in the command and control organization can continue to be transmitted smoothly, the damaged network topology needs to be repaired in a timely and effective manner. The problem of repairing military communication network under the circumstance of nodes are hit in the network is described. This paper propose a method to repair damaged network by adding edges to the damaged network topology, which aims at maximizing network invulnerability. Network topological connection cost is based on average connection degree. The repair model is established with the consideration of connect cost limit and network connectivity. The model solving method based on discrete artificial bee colony algorithm is designed. Finally, proposed repair method is tested and compared with other three edge addition repair methods with a specific case of network. The experimental results show that the proposed method could enhance damaged network's invulnerability and outperforms the compared methods.INDEX TERMS Command and control system, repair model, repair strategy, edge addition, Discrete Artificial Bee Colony algorithm, information flow.

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Section: Solving Methods Based On Discrete Artificial Bee Colony Algorithmmentioning

confidence: 99%

Repair Strategy of Military Communication Network Based on Discrete Artificial Bee Colony Algorithm

2020

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“…Considering the constraints imposed by capacity of knapsacks, this problem is a constrained binary optimization problem. Such problems arises in various real-world applications such as in a distributed computer systems, warehouse optimization, cargo loading and stock problems [5,26]. This problem is explicitly formulated as follows:…”

Section: Application To Knapsackmentioning

confidence: 99%

“…There exist different Industry 4.0 applications, in which the decision variables are binary rather than real values. Optimal task planning [3], flow shop scheduling [4] and assembly line design problems [5,6] are example applications of binary optimization algorithms in a digital manufacturing setup. The competition between different factories emerges mass production, while maintaining robustness and giving appropriate response to continuous design changes.…”

Section: Introductionmentioning

confidence: 99%

XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications

et al. 2020

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Industry 4.0 is the fourth generation of industry which will theoretically revolutionize manufacturing methods through the integration of machine learning and artificial intelligence approaches on the factory floor to obtain robustness and speed-up process changes. In particular, the use of the digital twin in a manufacturing environment makes it possible to test such approaches in a timely manner using a realistic 3D environment that limits incurring safety issues and danger of damage to resources. To obtain superior performance in an Industry 4.0 setup, a modified version of a binary gravitational search algorithm is introduced which benefits from an exclusive or (XOR) operator and a repository to improve the exploration property of the algorithm. Mathematical analysis of the proposed optimization approach is performed which resulted in two theorems which show that the proposed modification to the velocity vector can direct particles to the best particles. The use of repository in this algorithm provides a guideline to direct the particles to the best solutions more rapidly. The proposed algorithm is evaluated on some benchmark optimization problems covering a diverse range of functions including unimodal and multimodal as well as those which suffer from multiple local minima. The proposed algorithm is compared against several existing binary optimization algorithms including existing versions of a binary gravitational search algorithm, improved binary optimization, binary particle swarm optimization, binary grey wolf optimization and binary dragonfly optimization. To show that the proposed approach is an effective method to deal with real world binary optimization problems raised in an Industry 4.0 environment, it is then applied to optimize the assembly task of an industrial robot assembling an industrial calculator. The optimal movements obtained are then implemented on a real robot. Furthermore, the digital twin of a universal robot is developed, and its path planning is done in the presence of obstacles using the proposed optimization algorithm. The obtained path is then inspected by human expert and validated. It is shown that the proposed approach can effectively solve such optimization problems which arises in Industry 4.0 environment.

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“…If this random value is greater than a predefined perturbation parameter, the bit is set as 1. Ozmen et al [36] produced new sources in discrete space with substitution and shift operators.…”

Section: Introductionmentioning

confidence: 99%

A Locally Searched Binary Artificial Bee Colony Algorithm Based on Hamming Distance for Binary Optimization

Ozger

Bolat

Di̇ri̇

2020

Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Artificial Bee Colony is a population based, bio-inspired optimization algorithm that developed for continues problems. The aim of this study is to develop a binary version of the Artificial Bee Colony (ABC) Algorithm to solve feature subset selection problem on bigger data. ABC Algorithm, has good global search capability but there is a lack of local search in the algorithm. To overcome this problem, the neighbor selection mechanism in the employed bee phase is improved by changing the new source generation formula that has hamming distance based local search capacity. With a re-population strategy, the diversity of the population is increased and premature convergence is prevented. To measure the effectiveness of the proposed algorithm, fourteen datasets which have more than 100 features were selected from UCI Machine Learning Repository and processed by the proposed algorithm. The performance of the proposed algorithm was compared to three well-known algorithms in terms of classification error, feature size and computation time. The results proved that the increased local search ability improves the performance of the algorithm for all criteria.

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Optimum Assembly Sequence Planning System Using Discrete Artificial Bee Colony Algorithm

Cited by 22 publications

References 25 publications

Repair Strategy of Military Communication Network Based on Discrete Artificial Bee Colony Algorithm

Repair Strategy of Military Communication Network Based on Discrete Artificial Bee Colony Algorithm

XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications

A Locally Searched Binary Artificial Bee Colony Algorithm Based on Hamming Distance for Binary Optimization

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