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Modified blind naked mole-rat algorithm applied to electromagnetic design problems

Mohammad Taherdangkoo
Archives of Electrical Engineering | 2021 | vol. 70 | No 2 | 367-380
Keywords electromagnetic design problems global optimization meta-heuristic algorithm
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Abstract

In this paper,we proposed a modified meta-heuristic algorithm based on the blind naked mole-rat (BNMR) algorithm to solve the multiple standard benchmark problems. We then apply the proposed algorithm to solve an engineering inverse problem in the electromagnetic field to validate the results. The main objective is to modify the BNMR algorithm by employing two different types of distribution processes to improve the search strategy. Furthermore, we proposed an improvement scheme for the objective function and we have changed some parameters in the implementation of the BNMR algorithm. The performance of the BNMR algorithm was improved by introducing several new parameters to find the better target resources in the implementation of a modified BNMR algorithm. The results demonstrate that the changed candidate solutions fall into the neighborhood of the real solution. The results show the superiority of the propose method over other methods in solving various mathematical and electromagnetic problems.
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Bibliography

[1] Taherdangkoo M., Modified stem cells algorithm for Loney’s solenoid benchmark problem, International Journal Applied Electromagnetics and Mechanics, vol. 42, no. 3, pp. 437–445 (2013).
[2] Coelho L.D.S., Alotto P., Loney’s Solenoid Design Using an Artificial Immune Network with Local Search Based on the Simplex Method, IEEE Transactions on Magnetics, vol. 44, no. 6, pp. 1070–1073 (2008).
[3] Khan T.A., Sai Ho Ling, An improved gravitational search algorithm for solving an electromagnetic design problem, Journal of Computational Electronics, vol. 19, no. 2, pp. 773–779 (2020), DOI: 10.1007/s10825-020-01476-8.
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[10] Taherdangkoo M., Shirzadi M.H., Yazdi M., Bagheri M.H., A robust clustering method based on blind, naked mole-rats (BNMR) algorithm, Swarm and Evolutionary Computation, vol. 10, pp. 1–11 (2013).
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Authors and Affiliations

Mohammad Taherdangkoo
1
ORCID: ORCID
  1. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

Scheduling trucks in a multi-door cross-docking system with time windows

G. Ozden I. Saricicek
Bulletin of the Polish Academy of Sciences Technical Sciences | 2019 | 67 | No. 2 | 349-362 | DOI: 10.24425/bpas.2019.128113
Keywords multi-door cross-docking center time window logistics decision support system meta-heuristics
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Abstract

Cross-docking is a strategy that distributes products directly from a supplier or manufacturing plant to a customer or retail chain, reducing handling or storage time. This study focuses on the truck scheduling problem, which consists of assigning each truck to a door at the dock and determining the sequences for the trucks at each door considering the time-window aspect. The study presents a mathematical model for door assignment and truck scheduling with time windows at multi-door cross-docking centers. The objective of the model is to minimize the overall earliness and tardiness for outbound trucks. Simulated annealing (SA) and tabu search (TS) algorithms are proposed to solve large-sized problems. The results of the mathematical model and of meta-heuristic algorithms are compared by generating test problems for different sizes. A decision support system (DSS) is also designed for the truck scheduling problem for multi-door cross-docking centers. Computational results show that TS and SA algorithms are efficient in solving large-sized problems in a reasonable time.

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Authors and Affiliations

G. Ozden
I. Saricicek

The application of different optimization techniques and Artificial Neural Networks (ANN) for coal-consumption forecasting: a case study

Mustafa Seker Neslihan Unal Kartal Selin Karadirek Cevdet Bertan Gulludag
Gospodarka Surowcami Mineralnymi - Mineral Resources Management | 2022 | vol. 38 | No 2 | 77-112 | DOI: 10.24425/gsm.2022.141668
Keywords coal consumption meta-heuristic optimization Grey Wolf Optimization (GWO) particle swarm optimization whale optimization
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Abstract

The demand for energy on a global scale increases day by day. Unlike renewable energy sources, fossil fuels have limited reserves and meet most of the world’s energy needs despite their adverse environmental effects. This study presents a new forecast strategy, including an optimization-based S-curve approach for coal consumption in Turkey. For this approach, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) are among the meta-heuristic optimization techniques used to determine the optimum parameters of the S-curve. In addition, these algorithms and Artificial Neural Network (ANN) have also been used to estimate coal consumption. In evaluating coal consumption with ANN, energy and economic parameters such as installed capacity, gross generation, net electric consumption, import, export, and population energy are used for input parameters. In ANN modeling, the Feed Forward Multilayer Perceptron Network structure was used, and Levenberg-Marquardt Back Propagation has used to perform network training. S-curves have been calculated using optimization, and their performance in predicting coal consumption has been evaluated statistically. The findings reveal that the optimization-based S-curve approach gives higher accuracy than ANN in solving the presented problem. The statistical results calculated by the GWO have higher accuracy than the PSO, WOA, and GA with R 2 = 0.9881, RE = 0.011, RMSE = 1.079, MAE = 1.3584, and STD = 1.5187. The novelty of this study, the presented methodology does not need more input parameters for analysis. Therefore, it can be easily used with high accuracy to estimate coal consumption within other countries with an increasing trend in coal consumption, such as Turkey.
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Authors and Affiliations

Mustafa Seker
1
ORCID: ORCID
Neslihan Unal Kartal
2
Selin Karadirek
3
Cevdet Bertan Gulludag
3
  1. Sivas Cumhuriyet University, Turkey
  2. Burdur Mehmet Akif Ersoy University, Turkey
  3. Akdeniz University, Antalya, Turkey

Solving scheduling problems with integrated online sustainability observation using heuristic optimization

Anna Burduk Kamil Musiał Artem Balashov Andre Batako Andrii Safonyk
Bulletin of the Polish Academy of Sciences Technical Sciences | 2022 | 70 | 6 | e143830 | DOI: 10.24425/bpasts.2022.143830
Keywords production scheduling sustainable development genetic algorithm Tabu search meta-heuristics intelligent optimization methods of production systems
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Abstract

The paper deals with the issue of production scheduling for various types of employees in a large manufacturing company where the decision-making process was based on a human factor and the foreman’s know-how, which was error-prone. Modern production processes are getting more and more complex. A company that wants to be competitive on the market must consider many factors. Relying only on human factors is not efficient at all. The presented work has the objective of developing a new employee scheduling system that might be considered a particular case of the job shop problem from the set of the employee scheduling problems. The Neuro-Tabu Search algorithm and the data gathered by manufacturing sensors and process controls are used to remotely inspect machine condition and sustainability as well as for preventive maintenance. They were used to build production schedules. The construction of the Neuro-Tabu Search algorithm combines the Tabu Search algorithm, one of the most effective methods of constructing heuristic algorithms for scheduling problems, and a self-organizing neural network that further improves the prohibition mechanism of the Tabu Search algorithm. Additionally, in the paper, sustainability with the use of Industry 4.0 is considered. That would make it possible to minimize the costs of employees’ work and the cost of the overall production process. Solving the optimization problem offered by Neuro-Tabu Search algorithm and real-time data shows a new way of production management.
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Authors and Affiliations

Anna Burduk
1
ORCID: ORCID
Kamil Musiał
1
Artem Balashov
1
Andre Batako
2
Andrii Safonyk
3
ORCID: ORCID
  1. Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
  2. Liverpool John Moores University, Faculty of Engineering and Technology,70 Mount Pleasant Liverpool L3 3AF, UK
  3. National University of Water and Environmental Engineering, Department of Automation, Electrical Engineering and Computer-Integrated Technologies, Rivne 33000, Ukraine

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