scholarly journals Simulated Annealing Technique for Routing in a Rectangular Mesh Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Noraziah Adzhar ◽  
Shaharuddin Salleh
Keyword(s):  
Simulated Annealing ◽  
Shortest Path ◽  
Printed Circuit Boards ◽  
Optimal Path ◽  
Heuristic Method ◽  
Mesh Network ◽  
Routing Problem ◽  
Rectangular Mesh ◽  
Metaheuristic Methods ◽  
Cell Placement

In the process of automatic design for printed circuit boards (PCBs), the phase following cell placement is routing. On the other hand, routing process is a notoriously difficult problem, and even the simplest routing problem which consists of a set of two-pin nets is known to be NP-complete. In this research, our routing region is first tessellated into a uniformNx×Nyarray of square cells. The ultimate goal for a routing problem is to achieve complete automatic routing with minimal need for any manual intervention. Therefore, shortest path for all connections needs to be established. While classical Dijkstra’s algorithm guarantees to find shortest path for a single net, each routed net will form obstacles for later paths. This will add complexities to route later nets and make its routing longer than the optimal path or sometimes impossible to complete. Today’s sequential routing often applies heuristic method to further refine the solution. Through this process, all nets will be rerouted in different order to improve the quality of routing. Because of this, we are motivated to apply simulated annealing, one of the metaheuristic methods to our routing model to produce better candidates of sequence.

scholarly journals Routing problem in rectangular mesh network using shortest path based Greedy method

2019 ◽  
Vol 1358 ◽  
pp. 012079
Author(s):  
Noraziah Adzhar ◽  
Shaharuddin Salleh ◽  
Yuhani Yusof ◽  
Muhammad Azrin Ahmad
Keyword(s):  
Shortest Path ◽  
Mesh Network ◽  
Greedy Method ◽  
Routing Problem ◽  
Rectangular Mesh

scholarly journals A Class of Evolutionary Algorithms for Determining Shortest Path Routing in 5G Ultra Dense Heterogeneous Networks

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Shortest Path ◽  
Optimal Path ◽  
Pso Algorithm ◽  
Small Cells ◽  
5G Networks ◽  
Routing Problem ◽  
Shortest Path Routing ◽  
Individual Fitness ◽  
Evolutionary Optimization Algorithms

Ultra Dense Network (UDN), an important element of the upcoming 5G networks are characterised by extremely dynamic operations due to the presence and mobility of large number of users spread over small cells of varying sizes. It makes optimal path between the source/destination pairs for communication and data transmission be highly dynamic in nature and hence a challenging issue to deal with. Under such dynamic backdrops, routing procedures have to exhibit robustness, scalability and time efficiency in order to ensure seamless link reliability and Quality of Service (QOS) of the network. In the proposed work, the shortest optimal route of the source and destination pair is found using a combination of evolutionary optimization algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm and our novel hybrid PSOGA approach which searches for an optimized solution by determining cost functions of individual fitness state and comparing states generated between individual solutions. Application of all the three above mentioned algorithms to the Shortest Path Routing Problem in UDNs and the results obtained have shown that the hybrid PSO-GA comparatively provided enhanced optimized solution.

scholarly journals Pengembangan Algoritma Hybrid Restart Simulated Annealing with Variable Neighborhood Search (HRSA-VNS) untuk penyelesaian kasus Vehicle Routing Problem with Time Windows (VRPTW)

2017 ◽  
Vol 6 (1) ◽  
pp. 49
Author(s):  
Titi Iswari
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Variable Neighborhood Search ◽  
Simulated Annealing Algorithm ◽  
Time Windows ◽  
Heuristic Method ◽  
Neighborhood Search ◽  
Routing Problem ◽  
Time Required

<p><em>Determining the vehicle routing is one of the important components in existing logistics systems. It is because the vehicle route problem has some effect on transportation costs and time required in the logistics system. In determining the vehicle routes, there are some restrictions faced, such as the maximum capacity of the vehicle and a time limit in which depot or customer has a limited or spesific opening hours (time windows). This problem referred to Vehicle Routing Problem with Time Windows (VRPTW). To solve the VRPTW, this study developed a meta-heuristic method called Hybrid Restart Simulated Annealing with Variable Neighborhood Search (HRSA-VNS). HRSA-VNS algorithm is a modification of Simulated Annealing algorithm by adding a restart strategy and using the VNS algorithm scheme in the stage of finding neighborhood solutions (neighborhood search phase). Testing the performance of HRSA-VNS algorithm is done by comparing the results of the algorithm to the Best Known Solution (BKS) and the usual SA algorithm without modification. From the results obtained, it is known that the algorithm perform well enough in resolving the VRPTW case with the average differences are -2.0% with BKS from Solomon website, 1.83% with BKS from Alvarenga, and -2.2% with usual SA algorithm without any modifications.</em></p><p><em>Keywords : vehicle routing problem, time windows, simulated annealing, VNS, restart</em></p>

Obstacle-aware routing problem in a rectangular mesh network

2015 ◽  
Vol 9 ◽  
pp. 653-663 ◽  
Author(s):  
Noraziah Adzhar ◽  
Shaharuddin Salleh
Keyword(s):  
Mesh Network ◽  
Routing Problem ◽  
Rectangular Mesh

scholarly journals Multi-Robot Routing Problem with Min–Max Objective

Robotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 122
Author(s):  
Jennifer David ◽  
Thorsteinn Rögnvaldsson
Keyword(s):  
Simulated Annealing ◽  
Comparative Study ◽  
Bipartite Graph ◽  
Real World ◽  
Heuristic Method ◽  
Problem Formulation ◽  
Permutation Matrix ◽  
Routing Problem ◽  
Research Communities ◽  
Multi Robot

In this paper, we study the “Multi-Robot Routing problem” with min–max objective (MRR-MM) in detail. It involves the assignment of sequentially ordered tasks to robots such that the maximum cost of the slowest robot is minimized. The problem description, the different types of formulations, and the methods used across various research communities are discussed in this paper. We propose a new problem formulation by treating this problem as a bipartite graph with a permutation matrix to solve it. A comparative study is done between three methods: Stochastic simulated annealing, deterministic mean-field annealing, and a heuristic-based graph search method. Each method is investigated in detail with several data sets (simulation and real-world), and the results are analysed and compared with respect to scalability, computational complexity, optimality, and its application to real-world scenarios. The paper shows that the heuristic method produces results very quickly with good scalability. However, the solution quality is sub-optimal. On the other hand, when optimal or near-optimal results are required with considerable computational resources, the simulated annealing method proves to be more efficient. However, the results show that the optimal choice of algorithm depends on the dataset size and the available computational budget. The contribution of the paper is three-fold: We study the MRR-MM problem in detail across various research communities. This study also shows the lack of inter-research terminology that has led to different names for the same problem. Secondly, formulating the task allocation problem as a permutation matrix formulation (bipartite graph) has opened up new approaches to solve this problem. Thirdly, we applied our problem formulation to three different methods and conducted a detailed comparative study using real-world and simulation data.

scholarly journals Parallel Genetic Algorithm for Shortest Path Routing Problem with Collaborative Neighbors

2015 ◽  
Vol 37 ◽  
pp. 327
Author(s):  
Reza Roshani ◽  
Mohammad Karim Sohrabi
Keyword(s):  
Genetic Algorithm ◽  
Mutation Rate ◽  
Shortest Path ◽  
Migration Rate ◽  
Mesh Network ◽  
Alternative Methods ◽  
Parallel Genetic Algorithm ◽  
Routing Problem ◽  
Shortest Path Routing ◽  
Fine Grained

Shortest path routing is generally known as a kind of routing widely availed in computer networks nowadays. Although advantageous algorithms exist for finding the shortest path, however alternative methods may have their own supremacy. In this paper, parallel genetic algorithm for finding the shortest path routing is resorted to. In order to improve the computation time in this routing algorithm and to distribute the load balance between the processors as well, Fine-Grained parallel GA model is opted for. The proposed algorithm was simulated on Wraparound Mesh network topologies in different sizes. To this end, several experiments were anchored to identify the most influential parameters such as Migration rate, Mutation rate, and Crossover rate. The simulation result shows that best result of mutation rate is: about 0.02 and 0.03, and migration rate for transmission to the neighbor’s node is 3 of the best chromosomes. This study has already shown that through using performance-based GA which uses fine-grained parallel algorithms, timing germane shortest path routing can be improved.

scholarly journals Performance Comparison between Particle Swarm Optimization and Differential Evolution Algorithms for Postman Delivery Routing Problem

2021 ◽  
Vol 11 (6) ◽  
pp. 2703
Author(s):  
Warisa Wisittipanich ◽  
Khamphe Phoungthong ◽  
Chanin Srisuwannapa ◽  
Adirek Baisukhan ◽  
Nuttachat Wisittipanit
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Metaheuristic Methods ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms ◽  
Current Practices

Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods—particle swarm optimization (PSO) and differential evolution (DE)—were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO.

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