Comprehensive Study on Computational Methods for K-Shortest Paths Problem

Abstract

The application domains like network connection routing, highway and power line engineering, robot motion planning and other optimization problems require the computation of shortest path. Computations of K-shortest paths provide more (K-1) numbers of backup shortest paths for consideration, which enable the applicability of additional constraints on the particular domains. For instance, a biologist can determine the best of an alignment from the available instances of biological sequence alignments generated from more than one shortest paths computation. The purpose of this paper is to provide a comprehensive review of existing algorithms available for Kshortest paths computation. It will be useful for researcher to implement the effective K-shortest paths computation based on their matching computational requirements over the domain of their interest.

Implementing Dual Base Stations within an IoT Network for Sustaining the Fault Tolerance of an IoT Network through an Efficient Path Finding Algorithm

“…Kyle E. et al [46] studied the choice of a graph search algorithm to find the shortest path in a directed relation graph with error propagation (DRGEP and have compared the method with other algorithms that include depth-first search, basic and R-value-based breadth-first search (RBFS), and Dijkstra's algorithm and found that Dijkstra's algorithm combined with coefficient scaling approach most accurate results when applied to bio application. Kalyan Mohanta B. P. et al [47] presented a comprehensive review of the existing k-shortest algorithms and showed the computational efficiency of each of the algorithms. Andrej Brodnik et al [48] presented an all-pairs shortest path algorithm for directed acrylic graphs and arbitrary edge lengths.…”

A benchmark test problem toolkit for multi-objective path optimization

“…As is well known, in path optimization, it is a popular practice to modify already-found optimal/good paths in order to find more other optimal/good paths. For example, in the -shortest paths problem, the first shortest paths are often used to calculate the ( − 1)th shortest path, 1 ≤ < [56,57], and in dynamical path optimization, the optimal path of current time instant is often used to calculate the optimal path of next time instant [58,59]. The MOPOP generated by the model of Section 3 can reveal the disadvantage of such a popular practice in path optimization, and therefore demands developing more effective path optimization methods.…”

KernelGraph: Understanding the kernel in a graph

“…Because call path searching response time is proportional to the value of K , 21 the more paths we want to find in a graph, the longer time the algorithm will take. We have implemented an improved Yen’s KSP algorithm 18 without considering loopless algorithm.…”