Evolutionary Discrete Firefly Algorithm for Travelling Salesman Problem

Travelling Salesman ◽

Discrete Firefly Algorithm

Proses distribusi barang pada suatu industri memerlukan bentuk efisiensi dalam pemilihan jalur terpendek yang akan dilalui seorang salesman. Secara matematis, pemilihan jalur terpendek merupakan suatu permasalahan optimasi yang disebut Travelling Salesman Problem (TSP). Terdapat banyak metode yang dapat diterapkan untuk menemukan solusi dari TSP, salah satunya Evolutionary Discrete Firefly Algorithm (EDFA) yang merupakan metode metaheuristik terbaru yang ditemukan oleh Jati dan Suyanto (2011) sebagai perkembangan dari Firefly Algorithm (FA) yang hanya didesain untuk permasalahan kontinu. Penelitian ini bertujuan untuk menjelaskan penerapan EDFA dalam penyelesaian TSP. Data yang digunakan dalam penelitian ini merupakan data kasus TSP berupa koordinat titik kota yang diambil dari database TSP Libary (TSPLIB) dengan 7 jenis kasus berbeda yaitu Ulysses16, Ulysses22, Eil51, Berlin52, St70, Rat99, dan Gr202. Kasus-kasus tersebut diselesaikan dengan menerapkan EDFA untuk menemukan solusi optimalnya melalui beberapa langkah yang terdapat dalam algoritma ini. Penyelesaian kasus TSP melalui EDFA juga dilakukan menggunakan bantuan program simulasi untuk mempermudah pehitungan. Hasil penelitian menunjukkan bahwa EDFA sebagai perkembangan FA telah berhasil diterapkan untuk kasus TSP yang memiliki solusi dalam ruang diskrit. Simulasi program EDFA yang diterapkan pada kasus-kasus tersebut memberikan solusi lebih baik pada beberapa kasus dengan hasil jarak optimal yang lebih pendek dibandingkan jarak optimal yang telah ditemukan sebelumnya.Kata Kunci: TSP, EDFA, firefly, solusi optimal.

A Survey to Nature Inspired Soft Computing

International Journal of Information System Modeling and Design ◽

10.4018/ijismd.2017040107 ◽

2017 ◽

Vol 8 (2) ◽

pp. 112-133 ◽

Cited By ~ 1

Author(s):

Deepak Kumar ◽

Sushil Kumar ◽

Rohit Bansal ◽

Parveen Singla

Keyword(s):

Genetic Algorithms ◽

Particle Swarm Optimization ◽

Real World ◽

Firefly Algorithm ◽

Cuckoo Search ◽

Travelling Salesman ◽

Swarm Optimization ◽

Physical Chemical ◽

Time Problem

This article describes how swarm intelligence (SI) and bio-inspired techniques shape in-vogue topics in the advancements of the latest algorithms. These algorithms can work on the basis of SI, using physical, chemical and biological frameworks. The authors can name these algorithms as SI-based, inspired by biology, physics and chemistry as per the basic concept behind the particular algorithm. A couple of calculations have ended up being exceptionally effective and consequently have turned out to be the mainstream devices for taking care of real-world issues. In this article, the reason for this survey is to show a moderately complete list of the considerable number of algorithms in order to boost research in these algorithms. This article discusses Ant Colony Optimization (ACO), the Cuckoo Search, the Firefly Algorithm, Particle Swarm Optimization and Genetic Algorithms in detail. For ACO a real-time problem, known as Travelling Salesman Problem, is considered while for other algorithms a min-sphere problem is considered, which is well known for comparison of swarm techniques.

Solving Travelling Salesman Problem with a Hybrid Genetic Algorithm and Firefly Algorithm

Journal of Engineering and Applied Sciences ◽

10.36478/jeasci.2019.8379.8385 ◽

2019 ◽

Vol 14 (22) ◽

pp. 8379-8385

Author(s):

Moy’awiah Al-Shannaq ◽

Ameera Jaradat

Keyword(s):

Genetic Algorithm ◽

Firefly Algorithm ◽

Hybrid Genetic Algorithm ◽

Lecture Notes in Electrical Engineering - Fundamental Research in Electrical Engineering ◽

HMFA: A Hybrid Mutation-Base Firefly Algorithm for Travelling Salesman Problem

10.1007/978-981-10-8672-4_31 ◽

2018 ◽

pp. 413-427

Author(s):

Mohammad Saraei ◽

Parvaneh Mansouri

Keyword(s):

Firefly Algorithm ◽

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):

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 ◽

Ant Colony ◽

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):

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.