A fast optimization method based on a hierarchical strategy for the travelling salesman problem

This study describes an optimization method called Simulated Annealing. The Simulated Annealing method is widely used in various combinatorial optimization tasks. Simulated Annealing is a stochastic optimization method that can be used to minimize the specified cost function given a combinatorial system with multiple degrees of freedom. In this study the application of the Simulated Annealing method to a well - known task of combinatorial analysis, Travelling Salesman Problem, is demonstrated and an experiment aimed to find the shortest tour distances between educational institutions of Rēzekne Municipality is performed. It gives possibilities to analyze and search optimal schools' network in Rēzekne Municipality.

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Application of Genetic Algorithm on Travelling Salesman Person

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1048.526 ◽

2014 ◽

Vol 1048 ◽

pp. 526-530

Author(s):

Sambourou Massinanke ◽

Chao Zhu Zhang

Keyword(s):

Genetic Algorithm ◽

Travelling Salesman Problem ◽

Optimization Method ◽

Initial Population ◽

Travelling Salesman ◽

Minimum Path ◽

Minimum Number ◽

Permutation Problem ◽

The City ◽

Survival Of The Fittest

GA (Genetic algorithm) is an optimization method based on operators (mutation and crossover) utilizing a survival of the fittest idea. They are utilized favorably in various problems. (TSP) Travelling salesman problem is one of the famous studied. TSP is a permutation problem in which the aim is to determine the shortest tour between n different points (cities), otherwise, the problem aims to find a route covering all cities where that the total distance is minimal. In this study a single salesman travels to each of the cities and close the loop by returning to the city he started, the aim of this study is to determine the minimum number of generations in which salesman does the minimum path, cities are chosen at random as initial population. The new generations are then created iteratively till the proper path is attained.

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A Two-Way Parallel Slime Mold Algorithm by Flow and Distance for the Travelling Salesman Problem

Applied Sciences ◽

10.3390/app10186180 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6180

Author(s):

Meijiao Liu ◽

Yanhui Li ◽

Qi Huo ◽

Ang Li ◽

Mingchao Zhu ◽

...

Keyword(s):

Travelling Salesman Problem ◽

Optimization Method ◽

Slime Mold ◽

Parallel Optimization ◽

Flow Index ◽

Travelling Salesman ◽

Random Strategy ◽

Optimization Principle ◽

Culture Model ◽

Better Than

In order to solve the problem of poor local optimization of the Slime Mold Algorithm (SMA) in the Travelling Salesman Problem (TSP), a Two-way Parallel Slime Mold Algorithm by Flow and Distance (TPSMA) is proposed in this paper. Firstly, the flow between each path point is calculated by the “critical pipeline and critical culture” model of SMA; then, according to the two indexes of flow and distance, the set of path points to be selected is obtained; finally, the optimization principle with a flow index is improved with two indexes of flow and distance and added random strategy. Hence, a two-way parallel optimization method is realized and the local optimal problem is solved effectively. Through the simulation of Traveling Salesman Problem Library (TSPLIB) on ulysses16, city31, eil51, gr96, and bier127, the results of TPSMA were improved by 24.56, 36.10, 41.88, 49.83, and 52.93%, respectively, compared to SMA. Furthermore, the number of path points is more and the optimization ability of TPSMA is better. At the same time, TPSMA is closer to the current optimal result than other algorithms by multiple sets of tests, and its time complexity is obviously better than others. Therefore, the superiority of TPSMA is adequately proven.

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PENCARIAN RUTE TERBAIK UNTUK DISTRIBUSI BANK SAMPAH MENGGUNAKAN TRAVELLING SALESMAN PROBLEM (TSP) STUDI KASUS KOTA DENPASAR

Jurnal Teknologi Informasi dan Komputer ◽

10.36002/jutik.v3i2.298 ◽

2018 ◽

Vol 3 (2) ◽

Author(s):

I Wayan Supriana

Keyword(s):

Genetic Algorithm ◽

Selection Process ◽

Travelling Salesman Problem ◽

Optimization Method ◽

Variable Speed ◽

Travelling Salesman ◽

Real Situation ◽

Bank Management ◽

Waste Bank ◽

Route Search

ABSTRACT A garbage bank is a place used to collect disaggregated debris. The high enthusiasm of the society to become a bank customer is inversely proportional to the real situation where there are still a few people who become customers of garbage bank. The problem with the community is to collect their own garbage and deposit it to the garbage bank management. This garbage collection process should be done optimally so that the purpose of the establishment of waste banks can be achieved and the growth of garbage bank customers increases. So to overcome the problem of garbage picking done the best route search for waste bank distribution using Traveling Salasmen Problem (TSP). The optimization method for best path determination using genetic algorithm. Genetic algorithm is a method by utilizing variable speed in each path that influence the travel time in each way and utilizing natural selection process known as evolution process, cross breeding process or crossover function, mutation and individual improvement. The result of the best route search of waste bank distribution using Traveling Salesman Problem (TSP) shows the best route that must be passed by Denpasar garbage bank in the 6th generation with 331 minutes travel time. Keywords: Garbage Bank, Genetic, TSP, Crossover ABSTRAK Bank sampah adalah suatu tempat yang digunakan mengumpulkan sampah-sampah yang sudah dipilah- pilah. Antusias masyarakat yang tinggi menjadi nasabah bank sampah berbanding terbalik dengan keadaan sebenarnya dimana masih sedikit masyarakat yang menjadi nasabah bank sampah. Hal yang menjadi kendala masyarakat adalah mengumpulkan sampah sendiri dan menyetornya ke pihak pengelola bank sampah. Proses pengumpulan sampah ini haruslah dilakukan secara optimal agar tujuan dari dibentuknya bank sampah dapat tercapai dan pertumbuhan nasabah bank sampah meningkat. Maka untuk mengatasi masalah penjemputan sampah dilakukan pencarian rute terbaik untuk distribusi bank sampah menggunakan Travelling Salasmen Problem (TSP). Metode optimasi untuk penentuan jalur terbaik menggunakan algoritma genetika. Algoritma genetika merupakan metode dengan memanfaatkan variable kecepatan disetiap jalur yang mempengaruhi waktu tempuh disetiap jalan dan memanfaatkan proses seleksi alamiah yang dikenal dengan proses evolusi, proses perkawinan silang atau fungsi crossover, mutasi maupun perbaikan individu. Hasil dari penelitian pencarian rute terbaik distribusi bank sampah menggunakan Travelling Salesman Problem (TSP) menunjukkan rute terbaik yang harus dilalui bank sampah kota Denpasar pada generasi ke 6 dengan waktu tempuh 331 menit. Kata Kunci: bank sampah, genetika, TSP, crossover

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Travelling salesman problem on dilute lattices : visit to a fraction of cities

Journal de Physique ◽

10.1051/jphys:01989005003025500 ◽

1989 ◽

Vol 50 (3) ◽

pp. 255-261 ◽

Cited By ~ 2

Author(s):

P. Sen ◽

B.K. Chakrabarti

Keyword(s):

Travelling Salesman Problem ◽

Travelling Salesman

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Solving Travelling Salesman Problem using an Enhanced Ant Colony Algorithm

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i7.214222 ◽

2019 ◽

Vol 7 (7) ◽

pp. 214-222

Author(s):

S. Suriya ◽

M. Rahul Kumar

Keyword(s):

Ant Colony Algorithm ◽

Travelling Salesman Problem ◽

Ant Colony ◽

Travelling Salesman

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Tourist Route Optimization in the Context of Covid-19 Pandemic

Sustainability ◽

10.3390/su13105492 ◽

2021 ◽

Vol 13 (10) ◽

pp. 5492

Author(s):

Cristina Maria Păcurar ◽

Ruxandra-Gabriela Albu ◽

Victor Dan Păcurar

Keyword(s):

Travelling Salesman Problem ◽

Route Planning ◽

Route Optimization ◽

Travelling Salesman ◽

Social Distancing ◽

Tourist Destinations ◽

Innovative Method ◽

The Social ◽

Immediate Response

The paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can bring an important advantage in transforming a destination into a safer one in times of Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay length has been accelerated while the epidemic became a pandemic. Moreover, the wariness for future pandemics has brought into spotlight the issue of overcrowded attractions inside a destination at certain moments. The method presented in this paper proposes a backtracking algorithm, more precisely an adaptation of the travelling salesman problem. The method presented is aimed to facilitate the navigation inside a destination and to revive certain less-visited sightseeing spots inside a destination while facilitating conformation with the social distancing measures imposed for Covid-19 control.

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