scholarly journals Location problem of Osmia cornuta nesting aids for optimum pollination

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244610
Author(s):  
Juraj Pekár ◽  
Marian Reiff ◽  
Ivan Brezina
Keyword(s):  
Honey Bees ◽  
Crop Production ◽  
Ad Hoc ◽  
Location Problem ◽  
Differential Evolution Algorithm ◽  
Economic Output ◽  
Proposed Model ◽  
Evolution Algorithm ◽  
Economic Contribution ◽  
Agricultural Business

The economic contribution of insect pollinators is evident as they contribute to higher crop yield quantity and quality. The management of bee species is key to crop production, especially where wild and domesticated bees are in low abundance. Several bee species have been identified as possible candidates for replacing, or at least supplementing, the decreasing number of honey bees. Our research seeks to address the location problem as regards nesting aids for Osmia cornuta bees in orchards using mathematical programming models for determining the optimal location of nesting aids and optimizing the management of solitary bees. A differential evolution algorithm is used to solve a location model of Osmia cornuta nesting aids for optimum pollination. Instead of a random ad hoc location of nesting aids in an orchard, or at the edge of an orchard, we utilize an effective optimization tool to determine locations which will optimize pollination by alternative pollinators, and increase the economic output of an agricultural business. The importance of this proposed model is likely to increase with agricultural intensification, and the decrease of the numbers of wild pollinators.

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.

Calibration Zone for the Parameters of the Differential Evolution Algorithm and Its Application to a Real Burst Location Problem

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
A. Pérez-González ◽  
A. Badillo-Olvera ◽  
O. Begovich ◽  
J. Ruíz-León
Keyword(s):  
Differential Evolution ◽  
Location Problem ◽  
Heuristic Algorithms ◽  
Differential Evolution Algorithm ◽  
Iterative Technique ◽  
Location Task ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Calibration Parameters

Numerical problems are usually solved using heuristic algorithms, due to their simplicity and easy understanding. Nevertheless, most of these methods have calibration parameters that do not count with selection premises oriented to obtain the best performance for the algorithm. This paper introduces an iterative technique that deals with this problem, searching for the calibration parameters that improve the Differential Evolution (DE) algorithm. The application of the proposed technique is illustrated on a real burst location problem in a pipeline prototype. The obtained results show the good performance of the methodology proposed for the burst location task, including the mapping of the calibration parameters that ameliorate the searching process.

An Uncertain Location-Allocation Model

2011 ◽  
Vol 204-210 ◽  
pp. 449-452 ◽  
Author(s):  
Si Bo Ding
Keyword(s):  
Differential Evolution Algorithm ◽  
Strong Impact ◽  
Service Networks ◽  
Allocation Model ◽  
Hybrid Intelligent Algorithm ◽  
Location Allocation ◽  
Proposed Model ◽  
Evolution Algorithm ◽  
Service Call

In service networks, the location of servers and allocation of demand nodes to them have a strong impact on the congestion at each server. The previous efforts in this area have concentrated on enhancing the reliability and quality of service with a probabilistic and fuzzy orientation. This paper considers the uncertain nature of such services and utilizes uncertainty theory to develop an uncertain queuing maximal covering location-allocation model. Our model considers one type of service call, one type of server and includes one constraint on the quality of queue length. To solve the proposed model, we design a hybrid intelligent algorithm which integrates 99-method and differential evolution algorithm. Finally, a numerical example is performed to show the application of the model and the algorithm.

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.

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