A novel multi-objective evolutionary algorithm for shortest path routing problem

2011 ◽  
Vol 7 (3/4) ◽  
pp. 355 ◽  
Author(s):  
C. Chitra ◽  
Subbaraj Potti ◽  
P. Subbaraj
Keyword(s):  
Evolutionary Algorithm ◽  
Shortest Path ◽  
Routing Problem ◽  
Shortest Path Routing ◽  
Multi Objective

scholarly journals A Reduced Uncertainty-Based Hybrid Evolutionary Algorithm for Solving Dynamic Shortest-Path Routing Problem

2015 ◽  
Vol 24 (05) ◽  
pp. 1550067 ◽  
Author(s):  
Huseyin Kusetogullari ◽  
Md. Haidar Sharif ◽  
Mark S. Leeson ◽  
Turgay Celik
Keyword(s):  
Evolutionary Algorithm ◽  
Shortest Path ◽  
State Of The Art ◽  
Search Space ◽  
Packet Transmission ◽  
Routing Problem ◽  
Shortest Path Routing ◽  
Hybrid Evolutionary Algorithm ◽  
Shortest Network ◽  
Encoding Method

The need of effective packet transmission to deliver advanced performance in wireless networks creates the need to find shortest network paths efficiently and quickly. This paper addresses a reduced uncertainty-based hybrid evolutionary algorithm (RUBHEA) to solve dynamic shortest path routing problem (DSPRP) effectively and rapidly. Genetic algorithm (GA) and particle swarm optimization (PSO) are integrated as a hybrid algorithm to find the best solution within the search space of dynamically changing networks. Both GA and PSO share context of individuals to reduce uncertainty in RUBHEA. Various regions of search space are explored and learned by RUBHEA. By employing a modified priority encoding method, each individual in both GA and PSO are represented as a potential solution for DSPRP. A complete statistical analysis has been performed to compare the performance of RUBHEA with various state-of-the-art algorithms. It shows that RUBHEA is considerably superior (reducing the failure rate by up to 50%) to similar approaches with increasing number of nodes encountered in the networks.

Exploring the Runtime of an Evolutionary Algorithm for the Multi-Objective Shortest Path Problem

2010 ◽  
Vol 18 (3) ◽  
pp. 357-381 ◽  
Author(s):  
Christian Horoba
Keyword(s):  
Evolutionary Algorithm ◽  
Shortest Path ◽  
Fitness Function ◽  
Approximate Solutions ◽  
Shortest Path Problem ◽  
Worst Case ◽  
Multi Objective ◽  
Natural Vector ◽  
Hard Problems ◽  
Vector Valued

We present a natural vector-valued fitness function f for the multi-objective shortest path problem, which is a fundamental multi-objective combinatorial optimization problem known to be NP-hard. Thereafter, we conduct a rigorous runtime analysis of a simple evolutionary algorithm (EA) optimizing f. Interestingly, this simple general algorithm is a fully polynomial-time randomized approximation scheme (FPRAS) for the problem under consideration, which exemplifies how EAs are able to find good approximate solutions for hard problems. Furthermore, we present lower bounds for the worst-case optimization time.

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.

Sign in / Sign up

Export Citation Format

Share Document