Ant Colony Optimization for UAV-based Intelligent Pesticide Irrigation System

2021 ◽  
Author(s):  
Zhikai Gao ◽  
Jie Zhu ◽  
Haiping Huang ◽  
Yifan Yang ◽  
Xudong Tan
Keyword(s):  
Ant Colony Optimization ◽  
Irrigation System ◽  
Ant Colony

scholarly journals Optimasi Sistem Irigasi Lahan Tada Hujan menggunakan Algoritma Ant Colony Optimization berbasis Tenaga Surya

Petir ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 45-51
Author(s):  
Abdul Haris
Keyword(s):  
Ant Colony Optimization ◽  
Electric Power ◽  
Water Distribution ◽  
Irrigation System ◽  
Ant Colony ◽  
Soil Conditions ◽  
Limited Ability ◽  
Human Power ◽  
Agriculture Land ◽  
Potential Resource

Indonesia is a country that has vast agriculture and has a majority source of income as farmer. The agriculture area still cannot be optimized considering that still a lot of dry agriculture land and have not got good irrigation system, several problems that cause are still a lot of dry agriculture which higher position of irrigation system available and there are limited land so that if irrigation system built or DAM then operational coast outweigh of impact on the land, another problem occurs is limited ability of community for built independent and modern irrigation system so that assistance is needed and technology that can be utilized as resource for built independent and modern irrigation system. One of potential resource in Indonesia is energy of sun which can be converted to electric power, this is energy very much in Indonesia, so that can be using as energy to pump water from springs to be distributed to dry agriculture land. To be able to reduce human power and monitoring this paper using Ant Colony Optimization as computation system.  This algorithm used to optimized water distribution evenly on dry agriculture land which is adapted to the soil conditions.   

Implementasi Ant Colony Optimization (ACO) pada Sistem Irigasi Lahan Tadah Hujan

Kilat ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 336-348
Author(s):  
Abdul Haris ◽  
Trisma Juwita ◽  
Rosida Nur Aziza ◽  
Hengki Sikumbang ◽  
Riki Ruli A. Siregar
Keyword(s):  
Ant Colony Optimization ◽  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Irrigation System ◽  
Ant Colony ◽  
Irrigation Systems ◽  
Distribution Process ◽  
Soil Dryness ◽  
The Impact

The purpose of this research is to produce an optimal water distribution system for irrigation of rainfed land. The problem with conventional irrigation systems is that the water distribution process cannot be controlled and monitored automatically and in real time. The impact on water distribution becomes ineffective. The implementation of Ant Colony Optimization (ACO) is used in research as a method to determine the location or node based on the pheromone pattern of the soil dryness level at the sprinkler nodes to be distributed by the water flow, taking into account the criteria level on the soil as a trend of probability values ​​and determining the nodes according to the needs in the flow water. The results obtained from this study indicate that the data displayed is the level of dryness of each node, the volume of water in the reservoir, and the flow of water flowing. The ACO test shows the sequence of nodes that will be passed after the optimization process of water distribution in a rainfed irrigation system using the ACO method gets an error value calculated by the MAPE method of 43% so that it gets an accuracy value of 57%.

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Timetabling Problem ◽  
University Course ◽  
The University

Achievement of Cost-Effective Datasets Transference in The Cloud Through Ant Colony Optimization

2012 ◽  
Vol 3 (3) ◽  
pp. 122-125
Author(s):  
THAHASSIN C THAHASSIN C ◽  
◽  
A. GEETHA A. GEETHA ◽  
RASEEK C RASEEK C
Keyword(s):  
Ant Colony Optimization ◽  
Cost Effective ◽  
Ant Colony

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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