Ant based probabilistic routing with pheromone and antipheromone mechanisms

Sandalidis, Harilaos G.; Mavromoustakis, Constandinos X.; Stavroulakis, Peter

doi:10.1002/dac.630

Cited by 16 publications

(32 citation statements)

References 6 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

452

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

452

213

View full text Add to dashboard Cite

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“…In the coalition formation, it judges a number of matched nodes for the task and optimizes the structure of coalition. Ant colony optimization (ACO) [15,16] takes inspiration from the behaviors of real ants. Ants secrete a type of substance, which is called pheromone, on the ground to mark some favorable path.…”

Section: Coalition Generation and Optimizationmentioning

confidence: 99%

Task coalition formation and self‐adjustment in the wireless sensor networks

Yang

Qiu

et al. 2012

Int J Communication

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SUMMARYCoalition is an essential mechanism in the multi-agent systems in the research of task-oriented area. Selfinterested agents coordinate their behaviors in a coalition to pursue a common goal and obtain payoffs. We propose the clustering-based coalition formation and self-adjustment mechanisms for tasks in the wireless sensor network. Before coalition formation, the management center clusters attributes of sensors to reduce the scale of searching space during coalition formation. And then an improved MAX-MIN ant colony optimization algorithm is adopted to resolve the problem of coalition formation. If a coalition member fails to fulfill a task, it can sponsor a negotiation with some noncoalition nodes to execute coalition self-repairing autonomously. The stimulus-response mechanism of wasp colony is introduced to determine the probability of response to the task invitation to avoid consuming extra energy. Simulation results show that our model efficiently reduces energy consumption and network traffic, decreases the number of dead nodes, and prolongs the lifetime of the networks.

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“…The algorithm proposed in [11] combines ABC with a mechanism from dynamic programming, and allows ants to update pheromone not only towards their source node, but also towards intermediate nodes on their path. In [94], the authors extend ABC with probabilistic routing of call setups and the use of anti-pheromone, which allows ants to decrease pheromone in some cases, instead of increasing it.…”

Section: Connection-oriented Networkmentioning

confidence: 99%

Principles and applications of swarm intelligence for adaptive routing in telecommunications networks

Ducatelle

Gambardella

2010

Swarm Intell

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In the past few years there has been a lot of research on the application of swarm intelligence to the problem of adaptive routing in telecommunications networks. A large number of algorithms have been proposed for different types of networks, including wired networks and wireless ad hoc networks. In this paper we give an overview of this research area. We address both the principles underlying the research and the practical applications that have been proposed. We start by giving a detailed description of the challenges in this problem domain, and we investigate how swarm intelligence can be used to address them. We identify typical building blocks of swarm intelligence systems and we show how they are used to solve routing problems. Then, we present Ant Colony Routing, a general framework in which most swarm intelligence routing algorithms can be placed. After that, we give an extensive overview of existing algorithms, discussing for each of them their contributions and their relative place in this research area. We conclude with an overview of future research directions that we consider important for the further development of this field.

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Ant based probabilistic routing with pheromone and antipheromone mechanisms

Cited by 16 publications

References 6 publications

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

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

Task coalition formation and self‐adjustment in the wireless sensor networks

Principles and applications of swarm intelligence for adaptive routing in telecommunications networks

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