scholarly journals A Hybrid Grey Wolf Optimizer for Process Planning Optimization with Precedence Constraints

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7360
Author(s):  
Mijodrag Milosevic ◽  
Robert Cep ◽  
Lenka Cepova ◽  
Dejan Lukic ◽  
Aco Antic ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Process Planning ◽  
Experimental Studies ◽  
Precedence Constraints ◽  
Good Time ◽  
Grey Wolf Optimizer ◽  
Planning Problem ◽  
Grey Wolf ◽  
Operation Sequencing ◽  
Planning Optimization

Process planning optimization is a well-known NP-hard combinatorial problem extensively studied in the scientific community. Its main components include operation sequencing, selection of manufacturing resources and determination of appropriate setup plans. These problems require metaheuristic-based approaches in order to be effectively and efficiently solved. Therefore, to optimize the complex process planning problem, a novel hybrid grey wolf optimizer (HGWO) is proposed. The traditional grey wolf optimizer (GWO) is improved by employing genetic strategies such as selection, crossover and mutation which enhance global search abilities and convergence of the traditional GWO. Precedence relationships among machining operations are taken into account and precedence constraints are modeled using operation precedence graphs and adjacency matrices. Constraint handling heuristic procedure is adopted to move infeasible solutions to a feasible domain. Minimization of the total weighted machining cost of a process plan is adopted as the objective and three experimental studies that consider three different prismatic parts are conducted. Comparative analysis of the obtained cost values, as well as the convergence analysis, are performed and the HGWO approach demonstrated effectiveness and flexibility in finding optimal and near-optimal process plans. On the other side, comparative analysis of computational times and execution times of certain MATLAB functions showed that the HGWO have good time efficiency but limited since it requires more time compared to considered hybrid and traditional algorithms. Potential directions to improving efficiency and performances of the proposed approach are given in conclusions.

A Modified Genetic Algorithm for Total Processing Time Minimization in the Manufacturing Process Planning Problem

2013 ◽  
Vol 423-426 ◽  
pp. 2184-2189
Author(s):  
Can Tao Shi ◽  
Qi You Liu ◽  
Yu Zhuo Liu
Keyword(s):  
Genetic Algorithm ◽  
Process Planning ◽  
Manufacturing Process ◽  
Processing Time ◽  
Solution Space ◽  
Experimental Result ◽  
Planning Problem ◽  
Total Processing Time ◽  
Operation Sequencing ◽  
Manufacturing Process Planning

For optimization of manufacturing process planning problem in flexible manufacture system, a mathematical model is established with objective of minimizing total processing time. The genetic algorithm is applied to solve it with modifications: a segmented chromosome coding is adopted to represent the entire solution space; crossover operator and mutation operator are re-defined to make genetic algorithm suitable for the problem; a constraint adjustment algorithm is designed for the constrained operation sequencing optimization problem. The experimental result indicates that the proposed model and algorithm are feasible and effective.

Optimal configuration of photovoltaic power plant using grey wolf optimizer: A comparative analysis considering CdTe and c-Si PV modules

Solar Energy ◽  
2019 ◽  
Vol 188 ◽  
pp. 247-257 ◽  
Author(s):  
Tekai Eddine Khalil Zidane ◽  
Mohd Rafi Bin Adzman ◽  
Mohammad Faridun Naim Tajuddin ◽  
Samila Mat Zali ◽  
Ali Durusu
Keyword(s):  
Comparative Analysis ◽  
Power Plant ◽  
Optimal Configuration ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Photovoltaic Power ◽  
Pv Modules

Evaluation of the effectiveness of different image skeletonization methods in biometric security systems

2020 ◽  
Vol 10 ◽  
Author(s):  
Mariya Nazarkevych ◽  
Serhii Dmytruk ◽  
Volodymyr Hrytsyk ◽  
Olha Vozna ◽  
Anzhela Kuza ◽  
...  
Keyword(s):  
Experimental Studies ◽  
Fingerprint Recognition ◽  
Good Time ◽  
Gabor Filtering ◽  
Security Systems ◽  
Thinning Algorithm ◽  
Biometric Security ◽  
Recognition Quality ◽  
Control And Management ◽  
The Internet Of Things

Background: Systems of the Internet of Things are actively implementing biometric systems. For fast and high-quality recognition in sensory biometric control and management systems, skeletonization methods are used at the stage of fingerprint recognition. The analysis of the known skeletonization methods of Zhang-Suen, Hilditch, Ateb-Gabor with the wave skeletonization method has been carried out and it shows a good time and qualitative recognition results. Methods: The methods of Zhang-Suen, Hildich and thinning algorithm based on Ateb-Gabor filtration, which form the skeletons of biometric fingerprint images, are considered. The proposed thinning algorithm based on Ateb-Gabor filtration showed better efficiency because it is based on the best type of filtering, which is both a combination of the classic Gabor function and the harmonic Ateb function. The combination of this type of filtration makes it possible to more accurately form the surroundings where the skeleton is formed. Results: Along with the known ones, a new Ateb-Gabor filtering algorithm with the wave skeletonization method has been developed, the recognition results of which have better quality, which allows to increase the recognition quality from 3 to 10%. Conclusion: The Zhang-Suen algorithm is a 2-way algorithm, so for each iteration, it performs two sets of checks during which pixels are removed from the image. Zhang-Suen's algorithm works on a plot of black pixels with eight neighbors. This means that the pixels found along the edges of the image are not analyzed. Hilditch thinning algorithm occurs in several passages, where the algorithm checks all pixels and decides whether to replace a pixel from black to white if certain conditions are satisfied. This Ateb-Gabor filtering will provide better performance, as it allows to obtain more hollow shapes, organize a larger range of curves. Numerous experimental studies confirm the effectiveness of the proposed method.

scholarly journals Optimal Sizing of an Island Hybrid Microgrid Based on Improved Multi-Objective Grey Wolf Optimizer

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1581
Author(s):  
Wenqiang Zhu ◽  
Jiang Guo ◽  
Guo Zhao ◽  
Bing Zeng
Keyword(s):  
Power Supply ◽  
Tidal Current ◽  
System Optimization ◽  
Energy System ◽  
Energy Sources ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
Multi Objective

The hybrid renewable energy system is a promising and significant technology for clean and sustainable island power supply. Among the abundant ocean energy sources, tidal current energy appears to be very valuable due to its excellent predictability and stability, particularly compared with the intermittent wind and solar energy. In this paper, an island hybrid energy microgrid composed of photovoltaic, wind, tidal current, battery and diesel is constructed according to the actual energy sources. A sizing optimization method based on improved multi-objective grey wolf optimizer (IMOGWO) is presented to optimize the hybrid energy system. The proposed method is applied to determine the optimal system size, which is a multi-objective problem including the minimization of annualized cost of system (CACS) and deficiency of power supply probability (DPSP). MATLAB software is utilized to program and simulate the hybrid energy system. Optimization results confirm that IMOGWO is feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. Furthermore, comparison of hybrid systems with and without tidal current turbines is undertaken to confirm that the utilization of tidal current turbines can contribute to enhancing system reliability and reducing system investment, especially in areas with abundant tidal energy sources.

Optimizing cetane improver concentration in biodiesel-diesel blend via grey wolf optimizer algorithm

Fuel ◽  
2020 ◽  
Vol 273 ◽  
pp. 117784 ◽  
Author(s):  
Erol Ileri ◽  
Aslan Deniz Karaoglan ◽  
Sener Akpinar
Keyword(s):  
Grey Wolf Optimizer ◽  
Grey Wolf
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