scholarly journals UAV routing with genetic algorithm based matheuristic for border security missions

2021 ◽  
Vol 11 (2) ◽  
pp. 128-138
Author(s):  
Omer Ozkan ◽  
Muhammed Kaya
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Approach ◽  
Cost Effective ◽  
Good Alternative ◽  
Ease Of Use ◽  
Computational Studies ◽  
Routing Problem ◽  
Upper Limit ◽  
Minimum Number ◽  
Time And Being

In recent years, Unmanned Aerial Vehicles (UAVs) are a good alternative for the problem of ensuring the security of the borders of the countries. UAVs are preferred because of their speed, ease of use, being able to observe many points at the same time, and being more cost-effective in total compared to other security tools. This study is dealt with the problem of the use of UAVs for the security of the Turkey-Syria borderline which becomes sensitive in recent years and the problem is modeled as a UAV routing problem. To solve the problem, a Genetic Algorithm Based Matheuristic (GABM) approach has been developed and 12 scenarios have been created covering the departure bases, daily patrol numbers, and ranges of UAVs. GABM finds the minimum number of UAVs to use in scenarios with the help of a GA run first and tries to find the optimal routes for these UAVs. If GABM can find an optimal route for the determined UAV number, it decreases the UAV number and tries to solve the problem again. GABM proposes a hybrid approach in which a metaheuristic with a mathematical model works together and the metaheuristic sets an upper limit for the number of UAVs in the model. In computational studies, when compared GA with GABM it is seen that GABM has obtained good results and decreased the utilized number of UAVs (up to 400%) and their flight distances (up to 85.99%) for the problem in very short CPU times (max. 122.17 s. for GA and max. 46.39 s. for GABM in addition to GA).

A Hybrid Approach Based on LP Metric Method and Genetic Algorithm for the Vehicle-Routing Problem with Time Windows, Driver-Specific Times, and Vehicles-Specific Capacities

2018 ◽  
Vol 9 (4) ◽  
pp. 51-67 ◽  
Author(s):  
Ebrahim Asadi-Gangraj ◽  
Sina Nayeri
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Hybrid Approach ◽  
Mixed Integer ◽  
Computational Time ◽  
Routing Problem ◽  
Lp Metric

Due to increasing population, increasing number of vehicles as well as environmental pollution, planning vehicles efficiently one of important problems nowadays. This article proposes a Multi-Objective Mixed Integer Programming (MOMIP) model for the vehicle-routing problem with time windows, driver-specific times and vehicles-specific capacities (VRPTDV), a variant of the classical VRPT that uses driver-specific travel and service times and vehicles-specific capacity to model the familiarity of the different drivers with the customers to visit. The first objective function aims to minimize traveled distance and the second objective function minimizing working duration. Since the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amount of computational time using exact solution approaches. Hence, the hybrid approach based on LP metric method and genetic algorithm is proposed to solve the given problem.

scholarly journals Comparison of long-read sequencing technologies in interrogating bacteria and fly genomes

2021 ◽  
Author(s):  
Eric S Tvedte ◽  
Mark Gasser ◽  
Benjamin C Sparklin ◽  
Jane Michalski ◽  
Carl E Hjelmen ◽  
...  
Keyword(s):  
Bacterial Genome ◽  
Hybrid Approach ◽  
Cost Effective ◽  
Fruit Fly ◽  
Drosophila Ananassae ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
E Coli ◽  
Hybrid Approaches ◽  
Long Read

Abstract The newest generation of DNA sequencing technology is highlighted by the ability to generate sequence reads hundreds of kilobases in length. Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) have pioneered competitive long read platforms, with more recent work focused on improving sequencing throughput and per-base accuracy. We used whole-genome sequencing data produced by three PacBio protocols (Sequel II CLR, Sequel II HiFi, RS II) and two ONT protocols (Rapid Sequencing and Ligation Sequencing) to compare assemblies of the bacteria Escherichia coli and the fruit fly Drosophila ananassae. In both organisms tested, Sequel II assemblies had the highest consensus accuracy, even after accounting for differences in sequencing throughput. ONT and PacBio CLR had the longest reads sequenced compared to PacBio RS II and HiFi, and genome contiguity was highest when assembling these datasets. ONT Rapid Sequencing libraries had the fewest chimeric reads in addition to superior quantification of E. coli plasmids versus ligation-based libraries. The quality of assemblies can be enhanced by adopting hybrid approaches using Illumina libraries for bacterial genome assembly or polishing eukaryotic genome assemblies, and an ONT-Illumina hybrid approach would be more cost-effective for many users. Genome-wide DNA methylation could be detected using both technologies, however ONT libraries enabled the identification of a broader range of known E. coli methyltransferase recognition motifs in addition to undocumented D. ananassae motifs. The ideal choice of long read technology may depend on several factors including the question or hypothesis under examination. No single technology outperformed others in all metrics examined.

Enterprise distribution routing optimization with soft time windows based on genetic algorithm

2021 ◽  
pp. 1-11
Author(s):  
Kaixian Gao ◽  
Guohua Yang ◽  
Xiaobo Sun
Keyword(s):  
Genetic Algorithm ◽  
Customer Satisfaction ◽  
Optimization Problems ◽  
Rapid Development ◽  
Time Windows ◽  
Theory And Practice ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
A Company

With the rapid development of the logistics industry, the demand of customer become higher and higher. The timeliness of distribution becomes one of the important factors that directly affect the profit and customer satisfaction of the enterprise. If the distribution route is planned rationally, the cost can be greatly reduced and the customer satisfaction can be improved. Aiming at the routing problem of A company’s vehicle distribution link, we establish mathematical models based on theory and practice. According to the characteristics of the model, genetic algorithm is selected as the algorithm of path optimization. At the same time, we simulate the actual situation of a company, and use genetic algorithm to plan the calculus. By contrast, the genetic algorithm suitable for solving complex optimization problems, the practicability of genetic algorithm in this design is highlighted. It solves the problem of unreasonable transportation of A company, so as to get faster efficiency and lower cost.

scholarly journals Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Random Search ◽  
Automatic Generation ◽  
Superior Performance ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Network Systems ◽  
Routing Problem

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

scholarly journals Data-Driven Tuning of PID Controlled Piezoelectric Ultrasonic Motor

Actuators ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 148
Author(s):  
Sarah Makarem ◽  
Bülent Delibas ◽  
Burhanettin Koc
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Good Alternative ◽  
Data Driven ◽  
Grid Search ◽  
Single Source ◽  
Dual Frequency ◽  
Dual Source ◽  
Stage System ◽  
Piezoelectric Ultrasonic Motor

Ultrasonic motors employ resonance to amplify the vibrations of piezoelectric actuator, offering precise positioning and relatively long travel distances and making them ideal for robotic, optical, metrology and medical applications. As operating in resonance and force transfer through friction lead to nonlinear characteristics like creep and hysteresis, it is difficult to apply model-based control, so data-driven control offers a good alternative. Data-driven techniques are used here for iterative feedback tuning of a proportional integral derivative (PID) controller parameters and comparing between different motor driving techniques, single source and dual source dual frequency (DSDF). The controller and stage system used are both produced by the company Physik Instrumente GmbH, where a PID controller is tuned with the help of four search methods: grid search, Luus–Jaakola method, genetic algorithm, and a new hybrid method developed that combines elements of grid search and Luus–Jaakola method. The latter method was found to be quick to converge and produced consistent result, similar to the Luus–Jaakola method. Genetic Algorithm was much slower and produced sub optimal results. The grid search has also proven the DSDF driving method to be robust, less parameter dependent, and produces far less integral position error than the single source driving method.

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