scholarly journals Optimization Model for Biogas Power Plant Feedstock Mixture Considering Feedstock and Transportation Costs Using a Differential Evolution Algorithm

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1610 ◽  
Author(s):  
Danijel Topić ◽  
Marinko Barukčić ◽  
Dražen Mandžukić ◽  
Cecilia Mezei
Keyword(s):  
Power Plant ◽  
Differential Evolution ◽  
Optimization Model ◽  
Power Plants ◽  
Differential Evolution Algorithm ◽  
Transportation Costs ◽  
Proposed Model ◽  
Transportation Distance ◽  
Evolution Algorithm ◽  
Plant Feedstock

In this paper, an optimization model for biogas power plant feedstock mixture with respect to feedstock and transportation costs using a differential evolution algorithm (DEA) is presented. A mathematical model and an optimization problem are presented. The proposed model introduces an optimal mixture of different feedstock combinations in a biogas power plant and informs about the maximal transportation distance for each feedstock before being unprofitable. In the case study, the proposed model is applied to five most commonly used feedstock in biogas power plants in Croatia and Hungary. The research is performed for a situation when the biogas power plant is not owned by the farm owner but by a third party. An optimization procedure is performed for each scenario with a cost of methane production that does not exceed 0.75 EUR/m3 in 1 MWe biogas power plant. The results show the needed yearly amounts and the maximum transportation distance of each feedstock.

scholarly journals pSum-SaDE: A Modifiedp-Median Problem and Self-Adaptive Differential Evolution Algorithm for Text Summarization

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Rasim M. Alguliev ◽  
Ramiz M. Aliguliyev ◽  
Chingiz A. Mehdiyev
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
Median Problem ◽  
Proposed Model ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Self Adaptive

Extractive multidocument summarization is modeled as a modifiedp-median problem. The problem is formulated with taking into account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy summaries. To solve the optimization problem a self-adaptive differential evolution algorithm is created. Differential evolution has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability. This paper has found that self-adaptive differential evolution can efficiently find the best solution in comparison with the canonical differential evolution. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is competitive on the DUC2006 dataset.

scholarly journals Optimization of Wireless Sensor Networks Based on Differential Evolution Algorithm

2019 ◽  
Vol 15 (01) ◽  
pp. 183 ◽  
Author(s):  
Qing Wan ◽  
Ming-Jiang Weng ◽  
Song Liu
Keyword(s):  
Differential Evolution ◽  
Optimization Model ◽  
Optimization Problem ◽  
Optimal Path ◽  
Minimum Energy ◽  
Differential Evolution Algorithm ◽  
Path Optimization ◽  
Wireless Sensor ◽  
Advantages And Disadvantages ◽  
Evolution Algorithm

To study the optimization problem of wireless sensor network (WSN) based on differential evolution, the single objective differential evolution algorithm is applied and combined with the advantages and disadvantages crossover strategy. Firstly, the path optimization problem in WSN is analyzed, and the optimization model is established. Then, the differential evolution algorithm is used as the search tool to solve the minimum energy consumption in the path optimization model, that is, the optimal path problem. Finally, the comparison experiment is carried out on the classical algorithm genetic algorithm (GA), particle swarm optimization (PSO) and standard differential evolution (DE) algorithm. The results show that the performance of differential evolution algorithm based on crossover strategy is superior to or not worse than that of several contrast algorithms. It can be seen that the differential evolution algorithm based on advantage and disadvantage crossover strategy has good effectiveness.

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