Genetic Algorithm-Based Approach for Optimal Location of Transit Repair Vehicles on a Large Urban Network

2004 ◽  
Vol 1879 (1) ◽  
pp. 41-50 ◽  
Author(s):  
Matthew Karlaftis ◽  
Konstantinos Kepaptsoglou ◽  
Antony Stathopoulos ◽  
Manoj Jha ◽  
David Lovell ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimal Location ◽  
Urban Network

Optimal Image Watermarking using Hybrid Optimization Algorithm

2015 ◽  
Vol 5 (1) ◽  
pp. 27-47
Author(s):  
Satish Ramchandra Todmal ◽  
Suhas Haribhau Patil
Keyword(s):  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Image Watermarking ◽  
Optimal Location ◽  
Secret Data ◽  
Bee Colony ◽  
Hybrid Optimization Algorithm ◽  
Extraction Algorithm

Image watermarking is a process of embedding secret information into cover image to secure transmission of secret data. Literature presents several image watermarking techniques are based on different transformation such as, wavelet transform, Fourier transform and cosine transform. Most of the authors have been developed an embedding and extraction algorithm newly with the transformed image. Then, the optimal location for embedding the secret data was identified by using optimization algorithm. Accordingly, the authors have developed an optimal robust watermarking technique using genetic algorithm and wavelet transform. In the previous work, watermarks were embedded into the wavelet coefficients of HL and LH band after searching the optimal locations in order to improve both quality of watermarked image and robustness of the watermark. In this work, the authors have developed to improve the genetic algorithm by combining it with Artificial Bee Colony algorithm (ABC Algorithm). Here, they have used hybrid algorithm for finding of optimal location in watermarking process. Finally, the comparative evaluation of the hybrid algorithm will be done with the existing and previous technique using different images and the performance of the extended algorithm will be analyzed using the PSNR, NC with convergence rate.

Robust optimization model for sustainable supply chain for production and distribution of polyethylene pipe

2020 ◽  
Vol 15 (4) ◽  
pp. 1613-1653
Author(s):  
Jaber Valizadeh ◽  
Ehsan Sadeh ◽  
Zainolabedin Amini Sabegh ◽  
Ashkan Hafezalkotob
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Environmental Impacts ◽  
Closed Loop ◽  
Optimal Location ◽  
Computational Time ◽  
Closed Loop Supply Chain ◽  
Content Type ◽  
Proposed Model ◽  
Production And Distribution

Purpose In this study, the authors consider the key decisions in the design of the green closed-loop supply chain (CSLC) network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, in this paper is the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered. Design/methodology/approach In this study, the author consider the key decisions in the design of the green CLSC network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered. Findings The results indicate that the results obtained from the colonial competition algorithm have higher quality than the genetic algorithm. This quality of results includes relative percentage deviation and computational time of the algorithm and it is shown that the computational time of the colonial competition algorithm is significantly lower than the computational time of the genetic algorithm. Furthermore, the limit test and sensitivity analysis results show that the proposed model has sufficient accuracy. Originality/value Solid modeling of the green supply chain of the closed loop using the solid optimized method by Bertsimas and Sim. Development of models that considered environmental impacts to the closed loop supply chain. Considering the impact of the technology type in the manufacture of products and the recycling of waste that will reduce emissions of environmental pollutants. Another innovation of the model is the multi-cycle modeling of the closed loop of supply chain by considering the uncertainty and the fixed and variable cost of transport.

scholarly journals Optimal Location, Sizing, and Appropriate Technology Selection of Distributed Generators for Minimizing Power Loss Using Genetic Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
T. R. Ayodele ◽  
A. S. O. Ogunjuyigbe ◽  
O. O. Akinola
Keyword(s):  
Genetic Algorithm ◽  
Power Loss ◽  
Optimal Operation ◽  
Optimal Location ◽  
Appropriate Technology ◽  
Technology Selection ◽  
Synchronous Generators ◽  
Distributed Generator ◽  
Induction Generators ◽  
Distributed Generators

Genetic algorithm (GA) is utilized to select most suitable Distributed Generator (DG) technology for optimal operation of power system as well as determine the optimal location and size of the DG to minimize power loss on the network. Three classes of DG technologies, synchronous generators, asynchronous generators, and induction generators, are considered and included as part of the variables for the optimization problem. IEEE 14-bus network is used to test the applicability of the algorithm. The result reveals that the developed algorithm is able to successfully select the most suitable DG technology and optimally size and place the DGs to minimize power loss in the network. Furthermore, optimum multiple placement of DG is considered to see the possible impact on power loss in the network. The result reveals that multiple placements can further reduce the power loss in the network.

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