A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case

Tsai, Chun Wei; Tseng, Shih Pang; Chiang, Ming Chao; Yang, Chu-Sing; Hong, Tzung-Pei

doi:10.1155/2014/178621

Cited by 27 publications

(18 citation statements)

References 38 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The first type is an educated mutation which tries to satisfy the entirety of layover constraints by modifying the dispatching times of trips that violate the layover constraints. The second mutation type focuses on changing the dispatching times of a set of trips which is selected based on the common genes detection method proposed by Tsai et al [36]. This method helps to reduce the search space of the algorithm and speed up the optimization process.…”

Section: Solution Method-evolutionary Optimizationmentioning

confidence: 99%

Bus Operations Scheduling Subject to Resource Constraints Using Evolutionary Optimization

Gkiotsalitis¹,

Kumar²

2018

Informatics

View full text Add to dashboard Cite

Abstract:In public transport operations, vehicles tend to bunch together due to the instability of passenger demand and traffic conditions. Fluctuation of the expected waiting times of passengers at bus stops due to bus bunching is perceived as service unreliability and degrades the overall quality of service. For assessing the performance of high-frequency bus services, transportation authorities monitor the daily operations via Transit Management Systems (TMS) that collect vehicle positioning information in near real-time. This work explores the potential of using Automated Vehicle Location (AVL) data from the running vehicles for generating bus schedules that improve the service reliability and conform to various regulatory constraints. The computer-aided generation of optimal bus schedules is a tedious task due to the nonlinear and multi-variable nature of the bus scheduling problem. For this reason, this work develops a two-level approach where (i) the regulatory constraints are satisfied and (ii) the waiting times of passengers are optimized with the introduction of an evolutionary algorithm. This work also discusses the experimental results from the implementation of such an approach in a bi-directional bus line operated by a major bus operator in northern Europe.

show abstract

Section: Solution Method-evolutionary Optimizationmentioning

confidence: 99%

Bus Operations Scheduling Subject to Resource Constraints Using Evolutionary Optimization

Gkiotsalitis¹,

Kumar²

2018

Informatics

View full text Add to dashboard Cite

show abstract

“…Algoritma genetika (GA) adalah salah satu metode yang ideal dalam menyelesaikan masalah TSP. Hubungan antara TSP dengan GA dapat diketahui melalui sejarah perkembangannya yang dapat dijumpai dalam buku [5] dan [14] dan berbagai artikel terkait, misalnya artikel [13] dan [16]. Pada prinsipnya GA dapat digunakan untuk berbagai masalah NP-Complete seperti masalah knapsack, 3-SAT, a subset, and vertex cover.…”

Section: Pendahuluanunclassified

Optimisasi Travelling Salesman Problem dengan Algoritma Genetika pada Kasus Pendistribusian Barang PT. Pos Indonesia di Kota Bandar Lampung

Rohman¹,

Zakaria²,

Asmiati³

et al. 2020

jmi

View full text Add to dashboard Cite

Optimisasi merupakan sebuah proses untuk mendapatkan nilai minimum atau maksimum dari sebuah fungsi peluang. Salah satu masalah yang melibatkan proses optimisasi yaitu Travelling Salesman Problem (TSP). Studi kasus TSP yang dibahas dalam penelitian ini adalah berkenaan dengan permasalahan pendistribusian barang yang dilakukan oleh PT. Pos Indonesia di Kota Bandar Lampung. Beberapa permasalahan yang dimaksud antara lain keterlambatan pengiriman barang, kurang puasnya pelayanan kantor pos, rute pendistribusian barang yang rumit dan lain-lain. Penyelesaian masalah yang dihadapi oleh PT. Pos Indonesia dengan menggunakan konsep TSP yang dimaksud dapat diselesaikan dengan Metode Algoritma Genetika (algoritma pencarian yang didasarkan atas mekanisme seleksi alami dan evolusi biologis). Pengunaan metode Algoritma Genetika dalam menyelesaikan masalah yang dibahas memberikan hasil bahwa untuk mencapai posisi optimal proses pengujian yang menggunakan order crossover dan inversion mutation perlu dilakukan pengulangan proses sebanyak 8 kali. (80%).

show abstract

“…The difference is when using the genetic mutation operator. After the process of generating new solutions (lines [11][12][13][14][15][16][17] is completed, it is checked whether there are same solutions among them. When two identical solutions are found, the second one mutates (lines [18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Materials and Algorithmmentioning

confidence: 99%

Different Applications of the Genetic Mutation Operator for Symetric Travelling Salesman Problem

Kralev

2018

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

This paper presents the results of an analysis of three algorithms for the Travelling Salesman Problem (TSP). The basic steps of genetic algorithms (GAs) and their benefits in solving combinatorial optimization problems are also presented. Moreover, several studies related to TPS and some approaches to its solution are discussed. An optimized version of the standard recursive algorithm for solving TSP using the backtracking method is presented. This algorithm is used to generate optimal solutions concerning the studied graphs. In addition, a standard genetic algorithm for solving TSP and its modification are also presented. The modified algorithm uses the genetic operator mutation in a different way. The results show that the recursive algorithm can be used successfully to solve the TSP for graphs with a small number of vertices, for instance, 25-30. The results of the two GAs were different. The modified GA found the optimal solutions for all tested graphs, while the standard GA found the optimal solutions in only 40% of the cases. These results were obtained for a reasonable time (in seconds), with appropriate values of the control parameters-population size and reproduction number. It appeared that the use of the genetic mutation operator yields better results when applied to identical solutions. If pairs of identical solutions are found in a population, then every second must mutate. The methodology and the conditions for conducting the experiments are described in details.

show abstract

A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case

Cited by 27 publications

References 38 publications

Bus Operations Scheduling Subject to Resource Constraints Using Evolutionary Optimization

Bus Operations Scheduling Subject to Resource Constraints Using Evolutionary Optimization

Optimisasi Travelling Salesman Problem dengan Algoritma Genetika pada Kasus Pendistribusian Barang PT. Pos Indonesia di Kota Bandar Lampung

Different Applications of the Genetic Mutation Operator for Symetric Travelling Salesman Problem

Contact Info

Product

Resources

About