scholarly journals Developing an Android-Based City Tour App using Evolutionary Algorithm

2021 ◽  
Vol 15 (14) ◽  
pp. 193
Author(s):  
Abidatul Izzah ◽  
Irmala Arin Kusuma ◽  
Yudi Irawan ◽  
Toga Aldila Cinderatama ◽  
Benni Agung Nugroho
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Mobile Devices ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Brute Force ◽  
Running Time ◽  
Force Method ◽  
Brute Force Method ◽  
The City

Traveling around a city and making transit in certain areas is called a city tour. Furthermore, determining the optimal city tour route can be considered as a traveling salesman problem. There are many kinds of algorithms to solve this, one of which is the Genetic Algorithm (GA). In developing the City Tour application, a platform is needed to be taken to various places anywhere and anytime. Finally, we developed an application that runs on mobile devices. This application is built on the Android platform so that its use can be more efficient. Furthermore, it can be concluded that the GA applied to the Android-based City Tour Application is reliable to determine city tour routes; this is evidenced by comparing GA with the brute force method, where GA provides optimum results with less running time.

scholarly journals PENYELESAIAN MULTI TRAVELING SALESMAN PROBLEM DENGAN ALGORITMA GENETIKA

2017 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
NI KADEK MAYULIANA ◽  
EKA N. KENCANA ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Heuristic Algorithm ◽  
Minimum Distance ◽  
Processing Time ◽  
Traveling Salesman ◽  
Running Time ◽  
Shortest Route ◽  
Time Required

Genetic algorithm is a part of heuristic algorithm which can be applied to solve various computational problems. This work is directed to study the performance of the genetic algorithm (GA) to solve Multi Traveling Salesmen Problem (multi-TSP). GA is simulated to determine the shortest route for 5 to 10 salesmen who travelled 10 to 30 cities. The performance of this algorithm is studied based on the minimum distance and the processing time required for 10 repetitions for each of cities-salesmen combination. The result showed that the minimum distance and the processing time of the GA increase consistently whenever the number of cities to visit increase. In addition, different number of sales who visited certain number of cities proved significantly affect the running time of GA, but did not prove significantly affect the minimum distance.

scholarly journals Application of Genetic Algorithm for More Efficient Multi-Layer Thickness Optimization in Solar Cells

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1726
Author(s):  
Premkumar Vincent ◽  
Gwenaelle Cunha Sergio ◽  
Jaewon Jang ◽  
In Man Kang ◽  
Jaehoon Park ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Solar Cells ◽  
Heuristic Method ◽  
Brute Force ◽  
Force Parameter ◽  
Thickness Optimization ◽  
Sweep Method ◽  
Case Scenario ◽  
Force Method ◽  
Brute Force Method

Thin-film solar cells are predominately designed similar to a stacked structure. Optimizing the layer thicknesses in this stack structure is crucial to extract the best efficiency of the solar cell. The commonplace method used in optimization simulations, such as for optimizing the optical spacer layers’ thicknesses, is the parameter sweep. Our simulation study shows that the implementation of a meta-heuristic method like the genetic algorithm results in a significantly faster and accurate search method when compared to the brute-force parameter sweep method in both single and multi-layer optimization. While other sweep methods can also outperform the brute-force method, they do not consistently exhibit 100% accuracy in the optimized results like our genetic algorithm. We have used a well-studied P3HT-based structure to test our algorithm. Our best-case scenario was observed to use 60.84% fewer simulations than the brute-force method.

scholarly journals An Application of Genetic Algorithm in Determining Salesmen’s Routes: A Case Study

2018 ◽  
Vol 17 (1) ◽  
pp. 26
Author(s):  
Noufal Zhafira ◽  
Feri Afrinaldi ◽  
Taufik Taufik
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Pharmaceutical Products ◽  
Traveling Salesman ◽  
Future Research ◽  
Distribution Problem ◽  
Distribution Company ◽  
Time Required ◽  
The City

This paper presents a case study of determining vehicles’ routes. The case is taken from a pharmaceutical products distribution problem faced by a distribution company located in the city of Padang, Indonesia. The objective of this paper is to reduce the total distribution time required by the salesmen of the company. Since the company uses more than one salesman, then the problem is modeled as a multi traveling salesman problem (m-TSP). The problem is solved by employing genetic algorithm (GA) and a Matlab® based computer program is developed to run the algorithm. It is found that, by employing two salesmen only, the routes produced by GA results in a 30% savings in total distribution time compared to the current routes used by the company (currently the company employs three salesmen). This paper determines distances based on the latitude and longitude of the locations visited by the salesmen. Therefore, the distances calculated in this paper are approximations. It is suggested that actual distances are used for future research.

ASIF Approach to Solve the Green Traveling Salesman Problem

2018 ◽  
Vol 9 (1) ◽  
pp. 81-95 ◽  
Author(s):  
Ahmed Haroun Sabry ◽  
Jamal Benhra ◽  
Abdelkabir Bacha
Keyword(s):  
Objective Function ◽  
Ant Colony Optimization ◽  
Present Article ◽  
Traveling Salesman Problem ◽  
Real World ◽  
Transportation Problem ◽  
Problem Formulation ◽  
Traveling Salesman ◽  
Web Based ◽  
The City

The present article describes a contribution to solve transportation problems with green constraints. The aim is to solve an urban traveling salesman problem where the objective function is the total emitted CO2. We start by adapting ASIF approach for calculating CO2 emissions to the urban logistics problem. Then, we solve it using ant colony optimization metaheuristic. The problem formulation and solving will both work under a web-based mapping platform. The selected problem is a real-world NP-hard transportation problem in the city of Casablanca.

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