scholarly journals Genetic Algorithms Dynamic Population Size with Cloning in Solving Traveling Salesman Problem

2018 ◽  
Vol 2 (2) ◽  
pp. 87-100 ◽  
Author(s):  
Erna Budhiarti Nababan ◽  
Opim Salim Sitompul ◽  
Yuni Cancer
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Computational Time ◽  
Local Optimum ◽  
Travelling Salesman ◽  
Cloning Strategy ◽  
Population Sizes ◽  
Dynamic Population

Population size of classical genetic algorithm is determined constantly. Its size remains constant over the run. For more complex problems, larger population sizes need to be avoided from early convergence to produce local optimum. Objective of this research is to evaluate population resizing i.e. dynamic population sizing for Genetic Algorithm (GA) using cloning strategy. We compare performance of proposed method and traditional GA employed to Travelling Salesman Problem (TSP) of A280.tsp taken from TSPLIB. Result shown that GA with dynamic population size exceed computational time of traditional GA.

The Algorithm Animation of Genetic Algorithm of Travelling Salesman Problem

2013 ◽  
Vol 411-414 ◽  
pp. 2013-2016 ◽  
Author(s):  
Guo Zhi Wen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Algorithm Animation ◽  
The Traveling Salesman Problem

The traveling salesman problem is analyzed with genetic algorithms. The best route map and tendency of optimal grade of 500 cities before the first mutation, best route map after 15 times of mutation and tendency of optimal grade of the final mutation are displayed with algorithm animation. The optimal grade is about 0.0455266 for the best route map before the first mutation, but is raised to about 0.058241 for the 15 times of mutation. It shows that through the improvements of algorithms and coding methods, the efficiency to solve the traveling problem can be raised with genetic algorithms.

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 Genetic algorithm based approach to solve travelling salesman problem with one point crossover operator:

2013 ◽  
Vol 10 (3) ◽  
pp. 1393-1400 ◽  
Author(s):  
Sharadindu Roy
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Global Optimum ◽  
Traveling Salesman ◽  
Mutation Operator ◽  
Crossover Operator ◽  
Travelling Salesman ◽  
Apply Genetic ◽  
Multiple Copies ◽  
The Traveling Salesman Problem

In this paper, the travelling salesman problem using genetic algorithm has been attempted. In this practical paper solution is easy and we can easily apply genetic operator in this type of problem. Complexity is both in time and space, provided size of the problem an as integer (count is infinite). The solution of the traveling salesman problem is global optimum. There are cities and given distances between them. Traveling salesman has to visit all of them. TSP main objective is to find traveling sequence of cities to minimize the traveling distance.* traverse one time*initially we select parent1 & parent2 by Roulette wheel concept. Apply one point crossover operator on parents and produce the offspring. Again we apply the mutation operator on offspring and created child. But the no. of bits (cities) will be inverted by the mutation operator, that is depended on mutation probability (pm). So one generation contain 6 individual. Then count fitness of the individuals in each generation. For the next generation (for parent1 & parent2) two individuals will be selected whose fitness is best in generation. Here we see crossover between two good solution may not always yield a better or as good a solution. Since parents are good, so the probability of the child being good is high. Every time we have to do, identity the good solution in the population and make multiple copies of the good solution. 

A Lamarckian Genetic Algorithm Applied to the Travelling Salesman Problem

1999 ◽  
Vol 02 (04) ◽  
pp. 431-457 ◽  
Author(s):  
Bereket T. Tesfaldet ◽  
Augusto Y. Hermosilla
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Large Scale ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Local Optimum ◽  
Performance Improvements ◽  
Mutation Operators ◽  
Lamarckian Genetic Algorithm

Genetic Algorithms (GAs) comprise a class of adaptive heuristic search methods analogous to genetic inheritance and Darwinian strife for survivial of individuals within a population. Today, GAs are widely used to solve complex optimization problems, including ill-conditioned and NP-complete types arising in business, commerce, engineering, large-scale industries, and many other areas. To address these wide areas of applications and to improve upon their drawbacks, many variations and modifications of GAs have been proposed. The GA variation proposed in this paper has four basic operators: reproduction, recombination and two mutation operators, particularly applied to the famous and extensively studied Traveling Salesman Problem (TSP) in large-scale combinatorial optimization. Three of the operators use diversity information (standard deviation of costs) from the current population to adjust the diversity of the next population. The fourth one is an introduced new mutation operator called p-displacement that simulates the Lamarckian evolutionary learning and training concepts of gene improvement to bring chromosomes to their local optimum. We call the proposed GA: Lamarckian Genetic Algorithm-Traveling Salesman Problem (LGA-TSP). Emprical results show performance improvements compared to the classic and other modified GAs, as well as simulated annealing.

scholarly journals The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Zakir Hussain Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Hybrid Genetic Algorithm ◽  
Computational Time ◽  
Computational Results ◽  
Travelling Salesman ◽  
Solution Quality ◽  
Heuristic Solution ◽  
Present Solution ◽  
Sequencing Problems

The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances.

scholarly journals Using genetic algorithm to solve multiple traveling salesman problem and considering Carbon emissions

2020 ◽  
Vol 13 (36) ◽  
pp. 3707-3715
Author(s):  
Chris Jojo Obi ◽  
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Carbon Emission ◽  
Travelling Salesman Problem ◽  
Time Windows ◽  
Traveling Salesman ◽  
Transport Cost ◽  
Travelling Salesman ◽  
Insertion Method ◽  
Multiple Travelling Salesman Problem

Objectives: The Multiple Travelling Salesman problem is a complex combinatorial optimization problem which is a variance of the Traveling Salesman Problem,where a lot of salesmen are utilized in the solution. In this work a cold chain logistics and route optimization model with minimum transport cost, carbon cost and Refrigeration cost are constructed. Methods: A genetic algorithm is then proposed to solve for the Multiple Travelling Salesman Problem with time windows while transport cost, carbon emission cost and refrigeration cost is minimized. Findings: It was observed that the algorithm evolved towards the direction of the optimal value of the fitness function. Novelty: There are a number of studies that considered tournament selection strategy but just a few have applied genetic algorithm considering insertion method to solve a Multiple Travelling salesman Problem. This study uses insertion method to obtain optimal solution. Also, the researcher considered time windows, transport cost, carbon emission cost and refrigeration cost. Keywords: Genetic algorithm method; cold-logistics; multiple travelling salesman problem

scholarly journals Perancangan Aplikasi Penjadwalan Traveling Salesman Problem dengan Algoritma Genetika

2012 ◽  
Vol 3 (2) ◽  
pp. 977
Author(s):  
Hendy Tannady ◽  
Andrew Verrayo Limas
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Industry ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Operational Cost ◽  
Travelling Salesman ◽  
Optimal Sequence ◽  
Chain Management ◽  
Efficiency And Effectiveness ◽  
A Company

Supply chain management plays an important role in enhancing the efficiency and effectiveness of manufacturing industry business process. In this research, the problem is taken from a sales division in a company in determining the optimal sequence when delivering goods into nine cities. This problem is oftenreferred as travelling salesman problem. This problem is considered important since the optimal sequence can cut off operational cost. Creating an artificial intelligence for the company in determining the location and the optimal sequence of delivering goods is the main objective of this research. A genetic algorithm is utilized to determine the location and the optimal sequence. While for processing the data and concluding the result, researcher designed a Java-based application that provides the capability of automatic computing. The result of this computation is a sequence of locations with a fitness number for each. The best fitness number for the sequence location will be used for the final result and the conclusion to answer the company’s problem.

scholarly journals Experimental Study of a Hybrid Genetic Algorithm for the Multiple Travelling Salesman Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Maha Ata Al-Furhud ◽  
Zakir Hussain Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Hybrid Genetic Algorithm ◽  
Travelling Salesman ◽  
Complex Optimization ◽  
Problem Instances ◽  
Search Approach ◽  
Multiple Travelling Salesman Problem

The multiple travelling salesman problem (MTSP), an extension of the well-known travelling salesman problem (TSP), is studied here. In MTSP, starting from a depot, multiple salesmen require to visit all cities so that each city is required to be visited only once by one salesman only. It is NP-hard and is more complex than the usual TSP. So, exact optimal solutions can be obtained for smaller sized problem instances only. For large-sized problem instances, it is essential to apply heuristic algorithms, and amongst them, genetic algorithm is identified to be successfully deal with such complex optimization problems. So, we propose a hybrid genetic algorithm (HGA) that uses sequential constructive crossover, a local search approach along with an immigration technique to find high-quality solution to the MTSP. Then our proposed HGA is compared against some state-of-the-art algorithms by solving some TSPLIB symmetric instances of several sizes with various number of salesmen. Our experimental investigation demonstrates that the HGA is one of the best algorithms.

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