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

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.

scholarly journals A Genetic Method using Hybrid Crossover for Solving Travelling Salesman Problem

2019 ◽  
Vol 8 (2) ◽  
pp. 5066-5072
Keyword(s):  
Hybrid Method ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Performance Comparison ◽  
Initial Population ◽  
Genetic Approach ◽  
Comparison Standard ◽  
Travelling Salesman ◽  
Genetic Method ◽  
Better Than

This paper proposes a Genetic approach using Hybrid Crossover for Solving the Travelling Salesman Problem. Proposed hybrid method generates an initial population using Nearest Neighbor (NN) approach which is modified using “Sub-Path Mutation” (SPM) process. Modified population undergoes Distance Preserving Crossover (DPX) [2] and 2-opt Optimal mutation (2-opt) [1] to check for possible refinement. SPM searches position for the minimum distant city within a given path. This work is motivated by the algorithm developed by [3] who performed DPX and 2-opt mutation on the initial population generated using NN. For performance comparison, standard TSPLIB data is taken. The proposed hybrid method performances better in terms of % best error. It performs better than methods reported in [3 - 11].

scholarly journals SHORTEST PATH DETERMINATION BETWEEN EDUCATIONAL INSTITUTIONS OF RĒZEKNE MUNICIPALITY

2017 ◽  
Vol 3 ◽  
pp. 451
Author(s):  
Pēteris Grabusts ◽  
Jurijs Musatovs
Keyword(s):  
Simulated Annealing ◽  
Degrees Of Freedom ◽  
Travelling Salesman Problem ◽  
Optimization Method ◽  
Educational Institutions ◽  
Travelling Salesman ◽  
Simulated Annealing Method ◽  
Multiple Degrees Of Freedom ◽  
Combinatorial System ◽  
Annealing Method

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.

scholarly journals GENETIC ALGORITHM FOR OPTIMIZING DISTRIBUTION WITH ROUTE RESTRICTION CONSTRAINT DUE TO TRAFFIC JAMS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 295-301
Author(s):  
N. Mouttaki ◽  
J. Benhra ◽  
G. Rguiga
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Classical Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Simple Method ◽  
Traffic Jams ◽  
Better Than

Abstract. The Travelling Salesman Problem (TSP) is a classical problem in combinatorial optimization that consists of finding the shortest tour through all cities such that the salesman visits each city only one time and returns to the starting city. Genetic algorithm is one of the powerful ways to solve problems of traveling salesman problem TSP. The current genetic algorithm aims to take in consideration the constraints happening during the execution of genetic algorithm, such as traffic jams when solving TSP. This program has two important contributions. First one is proposing simple method into taking in consideration an inconvenient route linked to traffic jams. The second one is the use of closeness strategy during the initialization step, which can accelerate the execution time of the algorithm.The results of the experiments show that the improved algorithm works better than some other algorithms. The conclusion ends the analysis with recommendations and future works.

scholarly journals Comparative Study of Prim and Genetic Algorithms in Minimum Spanning Tree and Travelling Salesman Problem

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Andre Hasudungan Lubis
Keyword(s):  
Genetic Algorithm ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Travelling Salesman Problem ◽  
Operational Cost ◽  
Travelling Salesman ◽  
Maximum Value ◽  
Large Numbers ◽  
Crossover And Mutation ◽  
Better Than

Optimization is the essential thing in an algorithm. It can save the operational cost of an activity. At the Minimum Spanning Tree, the goal to be achieved is how all nodes are connected with the smallest weights. Several algorithms can calculate the use of weights in this graph. Genetic and Primary algorithms are two very popular algorithms for optimization. Prim calculates the weights based on the short-est distance from a graph. This algorithm eliminates the connected loop to minimize circuit. The nature of this algorithm is to trace all nodes to the smallest weights on a given graph. The genetic algorithm works by determining the random value as first initialization. This algorithm will perform selection, crossover, and mutation by the number of rounds specified. It is possible that this algorithm can not achieve the maximum value. The nature of the genetic algorithm is to work with probability. The results obtained are the most optimal results according to this algorithm. The results of this study indicate that the Prim is better than Genetics in determining the weights at the minimum spanning tree while Genetic algorithm is better for travelling salesman problem. Genetics will have maximum results when using large numbers of rotations and populations.

An Improvement Particle Swarm Optimization for Travelling Salesman Problem with the Mutation Operator

2015 ◽  
Vol 781 ◽  
pp. 527-530 ◽  
Author(s):  
Jaturong Sriborikit ◽  
Panwit Tuwanut
Keyword(s):  
Particle Swarm Optimization ◽  
Travelling Salesman Problem ◽  
Particle Swarm ◽  
Local Optimum ◽  
Mutation Operator ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Research Show ◽  
Better Than

This document proposed improvement PSO with applying mutation operator for solving Travelling Salesman Problem. To PSO solve or decrease trapping in local optimum. From experiment results of this research show that results of PSO with applying mutation operator obtain better than results of normal PSO for solving TSP.

Application of Genetic Algorithm on Travelling Salesman Person

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.

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

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

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