Optimasi Travelling Thief Problem Menggunakan Algoritma Tree Physiology Optimization Berbasis Hiper Heuristik

Travelling thief problem (TTP) merupakan gabungan dari permasalahan travelling salesman problem dan knapsack problem. travelling thief problem sendiri merupakan permasalahan NP-Hard sehingga permasalahan sebagian besar diselesaikan menggunakan algoritma heuristic dan terus berkembang seiring berjalanya waktu. Algoritma yang digunakan pada penelitian ini adalah simple random untuk pemilihan low level heuristic (LLH) dan tree physiology optimization (TPO) untuk langkah move acceptance dengan menggunakan model Hyper-Heuristics. Pada penelitian yang telah dilakukan sebelumnya algoritma TPO mampu menghasilkan nilai yang cukup kompetitif dengan waktu komputasi yang baik, sedangkan pemodelan Hyper-Heuristics dapat menghasilkan nilai yang konsisten pada data yang beragam. Penelitian diawali dengan memodelkan algoritma TPO menjadi Hyper-Heuristics dan diuji coba dengan data dari TSPLib. Dari hasil uji coba yang dilakukan dapat dilihat bagaimana performa algoritma baru pada data yang diuji. Berdasarkan hasil yang didapat dari penelitian ini dapat disimpulkan bahwa algoritma LLH TPO dapat mengolah data TTP dengan ukuran di bawah 100 dengan cukup baik terbukti dengan hasil yang lebih baik dari metode genetic programming based hyper-heuristic (GPHS) yang telah ada sebelumnya, namun pada data di atas 100 performa LLH TPO menurun jika dibandingkan dengan metode GPHS.

Download Full-text

Penentuan Rute Pengiriman Ice Tube di Kota Malang dengan Algoritma Genetika

Jurnal Buana Informatika ◽

10.24002/jbi.v11i1.2559 ◽

2020 ◽

Vol 11 (1) ◽

pp. 10

Author(s):

Nurina Savanti Widya Gotami ◽

Yane Marita Febrianti ◽

Robih Dini ◽

Hamim Fathul Aziz ◽

San Sayidul Akdam Augusta ◽

...

Keyword(s):

Genetic Algorithm ◽

Travelling Salesman Problem ◽

Combinatorial Problem ◽

Np Hard ◽

Travelling Salesman ◽

Shortest Route ◽

Reproduction Process ◽

Delivery Routes ◽

Np Hard Problem ◽

Selection Parameter

Abstract. Determining routes for ice tube delivery in Malang is a complex combinatorial problem classified as NP-hard problem. This study aims for optimizing the sales travel routes determination for the delivery to several customers by considering the efficiency of distance traveled. This problem is modeled in the form of Multi Salesman Traveling Problem. Genetic algorithm was used to optimize the determination of ice tube delivery routes that must be taken by each sales. Problems were coded by using permutation representation in which order crossover and swap mutation methods were used for the reproduction process. The process of finding solution was done by using elitism selection. The best genetic algorithm parameters obtained from the test results are the number of iterations of 40 and the population of 40, with the shortest route of 30.3 km. The final solution given by the genetic algorithm is in the form of a travel route that must be taken by each ice tube sales.Keywords: genetic algorithm, mutli travelling salesman problem, optimization, routeAbstrak. Penentuan rute pengiriman ice tube di kota Malang merupakan permasalahan kombinatorial kompleks yang diklasifikasikan sebagai permasalahan NP-hard. Penelitian ini bertujuan untuk melakukan optimasi dalam pembentukan rute perjalanan sales dalam melakukan pengiriman ke beberapa pelanggan dengan mempertimbangkan efisiensi jarak tempuh. Permasalahan ini dimodelkan dalam bentuk Multi Salesman Travelling Problem. Algoritme genetika digunakan untuk mengoptimalkan pembentukan rute pengiriman ice tube yang harus dilalui oleh setiap sales. Permasalahan dikodekan menggunakan representasi permutasi, dengan proses reproduksi menggunakan metode order crossover dan swap mutation. Proses pencarian solusi dilakukan menggunakan elitism selection. Parameter algoritme genetika terbaik yang didapatkan dari hasil pengujian adalah banyaknya iterasi sebesar 40 dan banyaknya populasi sebesar 40, dengan rute terpendek sebesar 30.3 km. Solusi akhir yang diberikan oleh algoritme genetika berupa rute perjalanan yang harus ditempuh oleh setiap sales ice tube.Kata Kunci: algoritme genetika, multi travelling salesman problem, optimasi, rute

Download Full-text

Polyhedral combinatorics of the cardinality constrained quadratic knapsack problem and the quadratic selective travelling salesman problem

Journal of Combinatorial Optimization ◽

10.1007/s10878-006-8462-5 ◽

2006 ◽

Vol 11 (4) ◽

pp. 421-434 ◽

Cited By ~ 4

Author(s):

Vicky Mak ◽

Tommy Thomadsen

Keyword(s):

Knapsack Problem ◽

Travelling Salesman Problem ◽

Polyhedral Combinatorics ◽

Travelling Salesman ◽

Quadratic Knapsack Problem ◽

Quadratic Knapsack

Download Full-text

Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem

Mathematics ◽

10.3390/math8112018 ◽

2020 ◽

Vol 8 (11) ◽

pp. 2018

Author(s):

Mohammed Mahrach ◽

Gara Miranda ◽

Coromoto León ◽

Eduardo Segredo

Keyword(s):

Evolutionary Algorithms ◽

Knapsack Problem ◽

Optimization Problems ◽

Travelling Salesman Problem ◽

Problem Formulation ◽

Travelling Salesman ◽

Multi Objective ◽

Main Components ◽

Pareto Fronts ◽

Single Objective

One of the main components of most modern Multi-Objective Evolutionary Algorithms (MOEAs) is to maintain a proper diversity within a population in order to avoid the premature convergence problem. Due to this implicit feature that most MOEAs share, their application for Single-Objective Optimization (SO) might be helpful, and provides a promising field of research. Some common approaches to this topic are based on adding extra—and generally artificial—objectives to the problem formulation. However, when applying MOEAs to implicit Multi-Objective Optimization Problems (MOPs), it is not common to analyze how effective said approaches are in relation to optimizing each objective separately. In this paper, we present a comparative study between MOEAs and Single-Objective Evolutionary Algorithms (SOEAs) when optimizing every objective in a MOP, considering here the bi-objective case. For the study, we focus on two well-known and widely studied optimization problems: the Knapsack Problem (KNP) and the Travelling Salesman Problem (TSP). The experimental study considers three MOEAs and two SOEAs. Each SOEA is applied independently for each optimization objective, such that the optimized values obtained for each objective can be compared to the multi-objective solutions achieved by the MOEAs. MOEAs, however, allow optimizing two objectives at once, since the resulting Pareto fronts can be used to analyze the endpoints, i.e., the point optimizing objective 1 and the point optimizing objective 2. The experimental results show that, although MOEAs have to deal with several objectives simultaneously, they can compete with SOEAs, especially when dealing with strongly correlated or large instances.

Download Full-text

On solving Travelling Salesman Problem with Vertex Requisitions

Yugoslav journal of operations research ◽

10.2298/yjor161012003e ◽

2017 ◽

Vol 27 (4) ◽

pp. 415-426 ◽

Cited By ~ 2

Author(s):

Anton Eremeev ◽

Yulia Kovalenko

Keyword(s):

Integer Programming ◽

Local Search ◽

Time Complexity ◽

Programming Model ◽

Travelling Salesman Problem ◽

Np Hard ◽

Travelling Salesman ◽

Binary Variables ◽

Integer Programming Model ◽

Almost All

We consider the Travelling Salesman Problem with Vertex Requisitions where, for each position of the tour, at most two possible vertices are given. It is known that the problem is strongly NP-hard. The algorithm, we propose for this problem, has less time complexity compared to the previously known one. In particular, almost all feasible instances of the problem are solvable in O(n) time using the new algorithm, where n is the number of vertices. The developed approach also helps in fast enumeration of a neighborhood in the local search and yields an integer programming model with O(n) binary variables for the problem.

Download Full-text

A Novel Metaheuristic for Travelling Salesman Problem

Journal of Industrial Engineering ◽

10.1155/2013/347825 ◽

2013 ◽

Vol 2013 ◽

pp. 1-5 ◽

Cited By ~ 2

Author(s):

Vahid Zharfi ◽

Abolfazl Mirzazadeh

Keyword(s):

Combinatorial Optimization ◽

Optimization Problems ◽

Travelling Salesman Problem ◽

Network Routing ◽

Np Hard ◽

Travelling Salesman ◽

Combinatorial Optimization Problems ◽

Hard Problems ◽

Insight Into ◽

Np Hard Problems

One of the well-known combinatorial optimization problems is travelling salesman problem (TSP). This problem is in the fields of logistics, transportation, and distribution. TSP is among the NP-hard problems, and many different metaheuristics are used to solve this problem in an acceptable time especially when the number of cities is high. In this paper, a new meta-heuristic is proposed to solve TSP which is based on new insight into network routing problems.

Download Full-text

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):

Travelling Salesman Problem ◽

Travelling Salesman

Download Full-text

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 ◽

Travelling Salesman Problem ◽

Ant Colony ◽

Travelling Salesman

Download Full-text

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):

Travelling Salesman Problem ◽

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.

Download Full-text