A GPS/ant-like routing algorithm for ad hoc networks

Camara, Daniel; Loureiro, A.A.F.

doi:10.1109/wcnc.2000.904807

Cited by 43 publications

(20 citation statements)

References 12 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

490

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

490

213

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“…The first appears to have been GPS/Ant-Link Algorithm (GPSAL) [38], though, along with some other early efforts [39,40], it is modeled after ants only in name -their protocols do not make use of any concepts from stigmergy.…”

Section: Ant-inspired Routing For Manetsmentioning

confidence: 99%

A taxonomy of biologically inspired research in computer networking

2010

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a b s t r a c tThe natural world is enormous, dynamic, incredibly diverse, and highly complex. Despite the inherent challenges of surviving in such a world, biological organisms evolve, self-organize, self-repair, navigate, and flourish. Generally, they do so with only local knowledge and without any centralized control. Our computer networks are increasingly facing similar challenges as they grow larger in size, but are yet to be able to achieve the same level of robustness and adaptability. Many research efforts have recognized these parallels, and wondered if there are some lessons to be learned from biological systems. As a result, biologically inspired research in computer networking is a quickly growing field. This article begins by exploring why biology and computer network research are such a natural match. We then present a broad overview of biologically inspired research, grouped by topic, and classified in two ways: by the biological field that inspired each topic, and by the area of networking in which the topic lies. In each case, we elucidate how biological concepts have been most successfully applied. In aggregate, we conclude that research efforts are most successful when they separate biological design from biological implementation -that is to say, when they extract the pertinent principles from the former without imposing the limitations of the latter.

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“…The ideas of using ACO to find an optimal path can be found in [17] and [31]- [33]. Suppose that there are two ants and two routes leading to a food source;…”

Section: A Acomentioning

confidence: 99%

“…Ants achieve stigmergic communication by laying a chemical substance called pheromone [15], [16] that induces changes in the environment which can be sensed by other ants. In recent years, computer scientists were able to transform the models of collective intelligence of ants into the useful optimization and control algorithms [13], [17]- [20]. For example, in the interesting and emerging field of ant colony optimization (ACO) [15], [17], [18], [21]- [23] a colony of ants is typically modeled as a society of mobile agents [24]- [27].…”

mentioning

confidence: 99%

“…In recent years, computer scientists were able to transform the models of collective intelligence of ants into the useful optimization and control algorithms [13], [17]- [20]. For example, in the interesting and emerging field of ant colony optimization (ACO) [15], [17], [18], [21]- [23] a colony of ants is typically modeled as a society of mobile agents [24]- [27]. ACO has been applied in many combinatorial optimization problems such as the asymmetric traveling salesman problem [28], [29], graph coloring problem [30], and vehicle routing problem [15].…”

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confidence: 99%

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Biologically Inspired Cooperative Routing for Wireless Mobile Sensor Networks

Iyengar

Chang

2007

IEEE Systems Journal

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Abstract-Biological systems present remarkable adaptation, reliability, and robustness in various environments, even under hostility. Most of them are controlled by the individuals in a distributed and self-organized way. These biological mechanisms provide useful resources for designing the dynamical and adaptive routing schemes of wireless mobile sensor networks, in which the individual nodes should ideally operate without central control. This paper investigates crucial biologically inspired mechanisms and the associated techniques for resolving routing in wireless sensor networks, including Ant-based and genetic approaches. Furthermore, the principal contributions of this paper are as follows. We present a mathematical theory of the biological computations in the context of sensor networks; we further present a generalized routing framework in sensor networks by diffusing different modes of biological computations using Ant-based and genetic approaches; finally, an overview of several emerging research directions are addressed within the new biologically computational framework.

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A GPS/ant-like routing algorithm for ad hoc networks

Cited by 43 publications

References 12 publications

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

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

A taxonomy of biologically inspired research in computer networking

Biologically Inspired Cooperative Routing for Wireless Mobile Sensor Networks

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