A new approach to dynamic network routing using Omicron Ant Colony algorithm

Verma, Om Prakash; Gupta, Nilesh; Sharma, Mohit; Nanda, Pankaj; Chawla, S.

doi:10.1109/icectech.2011.5941980

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…According to the characteristics of the processing and transmission components, the traffic flow pattern, and the performance expected to be delivered of the network, different types of routing problems can be defined. Mainly, there are two categories of network routing problems in which ACO has been applied: wired, e.g., wired best effort networks [211,212,213,214,215] and wired quality of service (QoS) networks [216,217,218,214,219,220], and wireless, e.g., mobile ad hoc networks [221,222,223,224,225]. The telecommunication algorithms simply rely on the adaptation capabilities of ACO.…”

Section: Discrete Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

492

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Discrete Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

492

213

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“…Verma et al [15] developed the Omicron Ant Colony Algorithm, based on the ACO framework, to provide a solution to the routing problem. Their algorithm was built on the principle of not updating the value of the pheromone per cycle but instead updating it after a specified number of iterations to reduce the time required to update the pheromone value.…”

Section: Introductionmentioning

confidence: 99%

Solving Routing Problem Using Improved Camel Herds Algorithm

Ardabili¹,

Ahmed²,

Abbood

2020

IJPCC

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This paper introduces a new adaptive, distributed routing algorithm based on the Improved Camel Herds Algorithm (CHA). It is an intelligent, multi-agent optimization algorithm that is inspired by the behavior of camels and how they search for food in their desert environment. We examine its ability to solve the routing problem in switched networks: finding the shortest path in the process of transferring data packets between networks. Many meta-heuristic algorithms have been previously proposed to address the routing problem, and this proposed approach is compared with three well-known algorithms (ACO, GA, PSO) on ten graphs (weighted, integer, and not negative) and datasets with various size of nodes (from 10 nodes to 297 nodes). Three performance criteria were used to evaluate the performance of the algorithms (mean relative error, standard deviation, and number of function evaluations). The results proved that the performance of the proposed algorithm is both promising and competitive with other algorithms.

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“…In addition to the above algorithms, there are some intelligent search algorithms, such as genetic algorithm, ant colony algorithm [12][13][14][15][16][17], particle swarm optimization [18][19], etc. But genetic algorithm require complex coding, ant colony algorithm based on pheromone concentration has probabilistic properties, particle swarm optimization algorithm given initial solution by adding disturbance.…”

Section: Introductionmentioning

confidence: 99%