Using the Bees Algorithm to solve combinatorial optimisation problems for TSPLIB

Ismail, Asrul Harun; Hartono, Natalia; Zeybek, Sultan; Pham, Duc Truong

doi:10.1088/1757-899x/847/1/012027

Cited by 28 publications

(31 citation statements)

References 21 publications

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“…In general, the positions of the members in the problem are changed according to certain rules, and new sequences are tried to be found with less cost. For the Bees Algorithm, Ismail et al [26] suggested three different operators in the local search section, namely, Swap, Insertion, and Inversion.…”

Section: The Online Bees Algorithmmentioning

confidence: 99%

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Using Online Bees Algorithm for Real-time Permutation Flow Shop Problem in Car Disassembly Line

Şahin¹,

Wang²

2021

Preprint

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The automobile disassembly line is a typical permutation flow shop problem (PFSP). In the automobile disassembly line, the goal of the research is to minimize the completion time of scheduling with optimizing the disassembly order of vehicles. However, disassembly time was uncertain at the initial stage as the consuming time of cars’ specific sequence only could be roughly estimated, which make the PFSP in automobile disassembly line unlike the traditional ones. In this study, the real-time PFSP problem in automobile disassembly line was defined and well solved with the proposed online Bees Algorithm (O-BA). The algorithm has been prepared in Matlab/Simulink to work in real-time. Time consumed by each component of each vehicle was roughly estimated based on engineer’s experience. First optimisation was carried out to decide the disassembly order of vehicles to be disassembled. When each component was disassembled, the real consuming time value was updated with the detecting system which was realized in Simulink. Then the O-BA was activated and created the new solution for the disassembly order of vehicles which were still not entering the disassembly line. The proposed O-BA algorithm has an online structure and conducted the optimisation with distinguishing whether the vehicles to be disassembled were entering the disassembly line. The result shows the O-BA with the detecting system was succeeded in realizing real-time PFSP in the disassembly line. Moreover, the method proposed in this study was suitable for solving the same kind of real-time PFSP in the assembly line or disassembly line.

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Section: The Online Bees Algorithmmentioning

confidence: 99%

“…In the swap process, the order of two different vehicle groups chosen at random is changed with each other [26]. As an example in Fig.…”

Section: The Online Bees Algorithmmentioning

confidence: 99%

Using Online Bees Algorithm for Real-time Permutation Flow Shop Problem in Car Disassembly Line

Şahin¹,

Wang²

2021

Preprint

View full text Add to dashboard Cite

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“…The basic BA, which was initially developed in 2005 to solve continuous problems, [ 5 ] has been improved to its combinatorial versions on solving PCB assembly, timetabling, machine scheduling, and TSP. [ 11,12 ] Combinatorial and continuous versions of BA principally have a similar general procedure. The only difference between the two versions is in the local search phase of the algorithm.…”

Section: The Combinatorial Bees Algorithm and Its Implementation To Tmentioning

confidence: 99%

“…The relationships’ assumption of parameter setting was taken from Pham et al. [ 11 ] that was also used by the previous study by Ismail et al. [ 12 ]…”

Section: The Combinatorial Bees Algorithm and Its Implementation To Tmentioning

confidence: 99%

Combinatorial Bees Algorithm for Vehicle Routing Problem

Ismail

Hartono

Zeybek

et al. 2021

Macromolecular Symposia

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Vehicle routing problem (VRP) is a combinatorial optimization problem that has been studied intensively for years. Although VRP can be solved using an exact method for small instances, a more complex one will be impractical due to the nature of the problem as an NP-hard problem. A metaheuristic is more suitable to solve this kind of problem because the near-optimal solution can be found in a relatively shorter time compared to the exact algorithm. Bees Algorithm (BA) as nature-inspired metaheuristic is used to find a near-optimal solution of VRP-TSPLIB's datasets. This study aims to provide the results of BA on the standard dataset of VRP. The BA has a very good performance, with 3.9% of the average Best-Error and 1.2 million of the average evaluations to reach the solution.

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“…Existing populationbased optimisation algorithms need to operate with large populations and, as a result, are generally slow. The Bees Algorithm [14] is a population-based algorithm that has been successfully employed to solve many complex real-world optimisation problems including continuous [15] and combinatorial [16] optimisation problems. It is able to find both local and global optima without needing to calculate the gradient of the objective function.…”

Section: Introductionmentioning

confidence: 99%

An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification

et al. 2021

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Recurrent neural networks (RNNs) are powerful tools for learning information from temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation approach is proposed for training deep RNNs for the sentiment classification task. The approach employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification problems by considering only three individual solutions in each iteration. BA-3+ combines the collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. Local learning with exploitative search utilises the greedy selection strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy of SVD. Global learning with explorative search achieves faster convergence without getting trapped at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and asymmetric distribution of the datasets from different domains, including Twitter, product reviews, and movie reviews. Comparative results have been obtained for advanced deep language models and Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE, and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have improved at least with a 30–40% improvement than the standard SGD algorithm for all classification datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks (RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the complex classification task, and it can handle the vanishing and exploding gradients problem of deep RNNs.

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Using the Bees Algorithm to solve combinatorial optimisation problems for TSPLIB

Cited by 28 publications

References 21 publications

Using Online Bees Algorithm for Real-time Permutation Flow Shop Problem in Car Disassembly Line

Using Online Bees Algorithm for Real-time Permutation Flow Shop Problem in Car Disassembly Line

Combinatorial Bees Algorithm for Vehicle Routing Problem

An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification

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