2014
DOI: 10.5121/ijcseit.2014.4405
|View full text |Cite
|
Sign up to set email alerts
|

An Improved Genetic Algorithm with a Local Optimization Strategy and an Extra Mutation Level for Solving Traveling Salesman Problem

Abstract: The Traveling salesman problem (TSP) is proved to be NP-complete

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0
1

Year Published

2018
2018
2024
2024

Publication Types

Select...
3
2
1

Relationship

0
6

Authors

Journals

citations
Cited by 8 publications
(4 citation statements)
references
References 17 publications
0
3
0
1
Order By: Relevance
“…Beberapa penelitan yang menunjukkan kemampuan GA untuk menemukan solusi yang optimal di berbagai bidang sudah banyak dibuktikan, seperti: penelitian di bidang distribusi logistik untuk menyelesaikan permasalahan Travelling Salesman Problem [6] dan [7], di bidang biologi untuk optimalisasi parameter dalam deteksi inti sel [8], di bidang rekayasa perangkat lunak untuk meakukan pengujian perangkat lunak [9], dan di bidang ekonomi untuk menentukan parameter pada fungsi produksi Cobb-Douglas [10].…”
Section: Pendahuluanunclassified
“…Beberapa penelitan yang menunjukkan kemampuan GA untuk menemukan solusi yang optimal di berbagai bidang sudah banyak dibuktikan, seperti: penelitian di bidang distribusi logistik untuk menyelesaikan permasalahan Travelling Salesman Problem [6] dan [7], di bidang biologi untuk optimalisasi parameter dalam deteksi inti sel [8], di bidang rekayasa perangkat lunak untuk meakukan pengujian perangkat lunak [9], dan di bidang ekonomi untuk menentukan parameter pada fungsi produksi Cobb-Douglas [10].…”
Section: Pendahuluanunclassified
“…In recent years, researchers have adjusted GAs to increase efficiency for application to new complex systems. For instance, researchers have focused on improving the selection operator to find optimal solutions to the multicast routing problem [1], performing a local search with the best offspring in a generation to quickly converge on the global optimum [7], improving the population initialization and crossover operator to segment magnetic resonance images [14], applying the Gaussian function with crossover and mutation to reduce computation time [15], and solving the traveling salesman problem by combining a GA and two local optimization strategies [16].…”
Section: Motivation and Related Workmentioning
confidence: 99%
“…Crossover operation means mating between a pair of solutions to generate a new pair [38]. A randomly selected cut point in parent solutions is determined and the tails of two parents are swapped to get new offspring [39] as in Fig. 2.…”
Section: B Crossover Operationmentioning
confidence: 99%