Application of Smell Detection Agent Based Algorithm for Optimal Path Identification by SDN Controllers

Self Cite

Node Disjoint Paths (NDP) is one of the extensively studied Graph Theory problem. In this problem, the input is a directed n vertex graph and the set of source destination pair of vertices. The goal is to find the maximum number of paths connecting each such pair, so that such discovered paths are node-disjoint. In this paper, a novel Canine Inspired Algorithm is proposed which is a bio-inspired one, based on the olfactory capabilities of canines in tracing and reaching a destination. Currently many of the existing algorithms try to identify disjoint paths in a linear manner, whereas the Canine algorithm can be executed in a concurrent manner, depending on the number of canines deployed to find the disjoint paths. The time complexity of the algorithm is estimated to be . We hope that this algorithm finds many applications in problems related to various fields such as communication networks, scheduling and transportation and provides better results.

Section: Related Workmentioning

confidence: 99%

Canine Algorithm for Node Disjoint Paths

Ammal

Sajimon

2020

Self Cite

“…In this paper, we propose Smell Detection Agent (SDA) algorithm that selects all paths and prioritizes the feasible paths. The algorithm is a nature inspired optimization algorithm suitable for identification of optimal paths with its priority in a graph [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Structural Testing Model Using SDA Algorithm

S¹,

2020

Path testing is the most needed and useful coverage criterion in structural testing. Tracing and obtaining the resultant paths is the main problem in path coverage testing. Evolutionary techniques are adopted in many software product evaluation methods such as generating and selection of input test data. The priority of the feasible paths is also to be determined. In this paper, we proposes an optimization algorithm for identifying the effective test data execution paths in control flow graph for the program module under test and finding the most efficient test paths using modified smell detection agent based optimization algorithm. New innovations are being conducted for bio-motivated algorithmic techniques from the characteristics of animal behavior. Smell detection agent based algorithm helps to identify most feasible paths and it uses sequential search to obtain all paths in a graph. The tester achieves the paths to be tested through a number of smell spot values from the source node to the target node. We will use control flow graph to produce perfect test paths and cyclomatic complexity number for obtaining the number of feasible test paths. The best feasible paths are prioritized using smell detection agent algorithm such that all the paths are thoroughly tested which ensures structural testing. This algorithm generates paths equal to the cyclomatic complexity. It can be illustrated that the proposed approach guarantees full path coverage.

“…The agent based techniques are useful in optimum allocation of packets or in load balancing of the container. Genetic algorithm and Smell detection based optimum allocation techniques are making fruitful solution to this problem [ 2 – 4 ]. These agents generates single optimum solution by an objective function after operating a sequence of steps.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Particle Swarm Optimisation for Cargo Packaging in Large Containers

Hareendran²,

S³

2020

Cargo management in all mode of transports like airlines, ships and trucks is a challenging task. The way in which an optimal allocation of packages in different containers are done using a software controlled method. An agent based software module is enabled as a service for the optimum allocation of cargo packages in the container terminals. There are multiple factors that will affect this allocation - size, shape, weight of the cargo packets and the container. When we design an optimal allocation module in a software these components need to be addressed along with capacity of the container. Hence, a multi-objective optimization algorithm will improve the performance of cargo management software. In this paper we suggest a Mixed Species Particle Swarm Optimisation (MSPSO) procedure for optimal allocation of cargo packages in containers of different size and capacity. The redesigned version of cargo management software performs well with search space on normal time complexity. The simulated results gives an improved optimised allocation than normalised allocation of cargo packets. The improved implementation performed better in terms of efficient cargo package allocation.