scholarly journals Exact and Local Search Methods for Solving Travelling Salesman Problem with Practical Application

2019 ◽  
Vol 60 (5) ◽  
pp. 1138-1153
Author(s):  
Sajjad Majeed Jasim ◽  
Faez Hassan Ali
Keyword(s):  
Local Search ◽  
Travelling Salesman Problem ◽  
Optimal Solution ◽  
Search Method ◽  
Practical Application ◽  
Travelling Salesman ◽  
Search Methods ◽  
Total Cost ◽  
Local Search Methods ◽  
The Traveling Salesman Problem

This paper investigates some exact and local search methods to solve the traveling salesman problem. The Branch and Bound technique (BABT) is proposed, as an exact method, with two models. In addition, the classical Genetic Algorithm (GA) and Simulated Annealing (SA) are discussed and applied as local search methods. To improve the performance of GA we propose two kinds of improvements for GA; the first is called improved GA (IGA) and the second is Hybrid GA (HGA). The IGA gives best results than GA and SA, while the HGA is the best local search method for all within a reasonable time for 5 ≤ n ≤ 2000, where n is the number of visited cities. An effective method of reducing the size of the TSP matrix was proposed with the existence of successive rules. The problem of the total cost of Iraqi cities was also discussed and solved by some methods in addition to local search methods to obtain the optimal solution.

scholarly journals Improved Chicken Swarm Optimization Algorithm to Solve the Travelling Salesman Problem

2018 ◽  
Vol 12 (3) ◽  
pp. 1054
Author(s):  
Fayçal Chebihi ◽  
Mohammed essaid Riffi ◽  
Amine Agharghor ◽  
Soukaina Cherif Bourki Semlali ◽  
Abdelfattah Haily
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Travelling Salesman Problem ◽  
Search Method ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Chicken Swarm Optimization ◽  
The Traveling Salesman Problem

<p>This paper proposes a novel discrete bio-inspired chicken swarm optimization algorithm (CSO) to solve the problem of the traveling salesman problem (TSP) which is one of the most known problems used to evaluate the performance of the new metaheuristics. This problem is solved by applying a local search method 2-opt in order to improve the quality of the solutions. The DCSO as a swarm system of the algorithm increases the level of diversification, in the same way the hierarchical order of the chicken swarm and the behaviors of chickens increase the level of intensification. In this contribution, we redefined the basic different operators and operations of the CSO algorithm. The performance of the algorithm is tested on a symmetric TSP benchmark dataset from TSPLIB library. Therefore, the algorithm provides good results in terms of both optimization accuracy and robustness comparing to other metaheuristics.</p>

scholarly journals Neutrosophic Triangular Fuzzy Travelling Salesman Problem Based on Dhouib-Matrix-TSP1 Heuristic

2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Souhail Dhouib
Keyword(s):  
Real World ◽  
Fuzzy Number ◽  
Travelling Salesman Problem ◽  
Numerical Test ◽  
Optimal Solution ◽  
Triangular Fuzzy Number ◽  
Travelling Salesman ◽  
Total Cost ◽  
Fuzzy Environment ◽  
Neutrosophic Number

In this paper, the Travelling Salesman Problem is considered in neutrosophic environment which is more realistic in real-world industries. In fact, the distances between cities in the Travelling Salesman Problem are presented as neutrosophic triangular fuzzy number. This problem is solved in two steps: At first, the Yager’s ranking function is applied to convert the neutrosophic triangular fuzzy number to neutrosophic number then to generate the crisp number. At second, the heuristic Dhouib-Matrix-TSP1 is used to solve this problem. A numerical test example on neutrosophic triangular fuzzy environment shows that, by the use of Dhouib-Matrix-TSP1 heuristic, the optimal or a near optimal solution as well as the crisp and fuzzy total cost can be reached.

scholarly journals Planning capacity for 5G and beyond wireless networks by discrete fireworks algorithm with ensemble of local search methods

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Hafiz Munsub Ali ◽  
Jiangchuan Liu ◽  
Waleed Ejaz
Keyword(s):  
Wireless Networks ◽  
Local Search ◽  
Wireless Network ◽  
Statistical Tests ◽  
Mathematical Framework ◽  
Integer Programming Problem ◽  
Search Methods ◽  
Fireworks Algorithm ◽  
Local Search Methods ◽  
Planning Capacity

Abstract In densely populated urban centers, planning optimized capacity for the fifth-generation (5G) and beyond wireless networks is a challenging task. In this paper, we propose a mathematical framework for the planning capacity of a 5G and beyond wireless networks. We considered a single-hop wireless network consists of base stations (BSs), relay stations (RSs), and user equipment (UEs). Wireless network planning (WNP) should decide the placement of BSs and RSs to the candidate sites and decide the possible connections among them and their further connections to UEs. The objective of the planning is to minimize the hardware and operational cost while planning capacity of a 5G and beyond wireless networks. The formulated WNP is an integer programming problem. Finding an optimal solution by using exhaustive search is not practical due to the demand for high computing resources. As a practical approach, a new population-based meta-heuristic algorithm is proposed to find a high-quality solution. The proposed discrete fireworks algorithm (DFWA) uses an ensemble of local search methods: insert, swap, and interchange. The performance of the proposed DFWA is compared against the low-complexity biogeography-based optimization (LC-BBO), the discrete artificial bee colony (DABC), and the genetic algorithm (GA). Simulation results and statistical tests demonstrate that the proposed algorithm can comparatively find good-quality solutions with moderate computing resources.

scholarly journals Solving Scheduling Problem in a Distributed Manufacturing System Using a Discrete Fruit Fly Optimization Algorithm

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3260 ◽  
Author(s):  
Zhang ◽  
Liu ◽  
Tang ◽  
Królczyk ◽  
Li
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Small Scale ◽  
Fruit Fly Optimization Algorithm ◽  
Distributed Manufacturing ◽  
Scheduling Optimization ◽  
Search Methods ◽  
Fruit Fly Optimization ◽  
Local Search Methods

This study attempts to optimize the scheduling decision to save production cost (e.g., energy consumption) in a distributed manufacturing environment that comprises multiple distributed factories and where each factory has one flow shop with blocking constraints. A new scheduling optimization model is developed based on a discrete fruit fly optimization algorithm (DFOA). In this new evolutionary optimization method, three heuristic methods were proposed to initialize the DFOA model with good quality and diversity. In the smell-based search phase of DFOA, four neighborhood structures according to factory reassignment and job sequencing adjustment were designed to help explore a larger solution space. Furthermore, two local search methods were incorporated into the framework of variable neighborhood descent (VND) to enhance exploitation. In the vision-based search phase, an effective update criterion was developed. Hence, the proposed DFOA has a large probability to find an optimal solution to the scheduling optimization problem. Experimental validation was performed to evaluate the effectiveness of the proposed initialization schemes, neighborhood strategy, and local search methods. Additionally, the proposed DFOA was compared with well-known heuristics and metaheuristics on small-scale and large-scale test instances. The analysis results demonstrate that the search and optimization ability of the proposed DFOA is superior to well-known algorithms on precision and convergence.

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