A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems

Reina, Daniel Gutiérrez; Ruiz, Patricia; Ciobanu, Radu-Ioan; Toral, S. L.; Dorronsoro, Bernabè; Dobre, Ciprian

doi:10.1155/2016/2082496

Cited by 30 publications

(15 citation statements)

References 84 publications

(115 reference statements)

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“…Coordinating a swarm of uavs to provide continuous coverage of an area of interest, identified as an intractable problem [38], can be effectively handled by evolutionary bio-inspired techniques. An adequate deployment of a swarm of uavs can be obtained by applying particle swarm optimization techniques [7] while path planning for multiobjective missions can be achieved using genetic algorithms [33,40] and artificial ant colony optimization [49] methods. A leader-follower coalition formation in swarms with large number of uavs, each with limited communication and energy capabilities, was proposed in [29] by employing quantum genetic algorithms.…”

Section: Bio-inspired Computation For Swarms and Uavsmentioning

confidence: 99%

Artificial intelligence and game theory controlled autonomous UAV swarms

Kusyk

Samoylov

et al. 2020

Evol. Intel.

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Autonomous unmanned aerial vehicles (uavs) operating as a swarm can be deployed in austere environments, where cyber electromagnetic activities often require speedy and dynamic adjustments to swarm operations. Use of central controllers, uav synchronization mechanisms or pre-planned set of actions to control a swarm in such deployments would hinder its ability to deliver expected services. We introduce artificial intelligence and game theory based flight control algorithms to be run by each autonomous uav to determine its actions in near real-time, while relying only on local spatial, temporal and electromagnetic (em) information. Each uav using our flight control algorithms positions itself such that the swarm maintains mobile ad-hoc network (manet) connectivity and uniform asset distribution over an area of interest. Typical tasks for swarms using our algorithms include detection, localization and tracking of mobile em transmitters. We present a formal analysis showing that our algorithms can guide a swarm to maintain a connected manet, promote a uniform network spreading, while avoiding overcrowding with other swarm members. We also prove that they maintain manet connectivity and, at the same time, they can lead a swarm of autonomous uavs to follow or avoid an em transmitter. Simulation experiments in opnet modeler verify the results of formal analysis that our algorithms are capable of providing an adequate area coverage over a mobile em source and maintain manet connectivity. These algorithms are good candidates for civilian and military applications that require agile responses to the changes in dynamic environments for tasks such as detection, localization and tracking mobile em transmitters.

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Section: Bio-inspired Computation For Swarms and Uavsmentioning

confidence: 99%

Artificial intelligence and game theory controlled autonomous UAV swarms

Kusyk

Samoylov

et al. 2020

Evol. Intel.

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“…Evolutionary algorithms (EA) are a biologically inspired non-gradient optimization technique that allows the rapid and efficient exploration of vast solution space. Evolutionary algorithms have been successfully applied to multiple problem domains including computational fluid dynamics, mobile network design, utility network design, control optimization, mathematical analysis, and production scheduling (McCorkle, et al, 2003;Reina, et al, 2016;Diaz-Dorado, et al, 2002;Zeidler, et al, 2001;Ishibuchi & Murata, 1998;Kirstukas, et al, 2005). Major components of an EA are a population of potential solutions (chromosomes), mechanisms to select, mate, and exchange portions of solutions with each other, and a means to evaluate solution fitness.…”

Section: Related Researchmentioning

confidence: 99%

A Pareto Based Multi-Objective Evolutionary Algorithm Approach to Military Installation Rail Infrastructure Investment

Lesinski¹,

Corns

2019

ISER

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Decision making for military railyard infrastructure is an inherently multi-objective problem, balancing cost versus capability. In this research, a Pareto-based Multi-Objective Evolutionary Algorithm is compared to a military rail inventory and decision support tool (RAILER). The problem is formulated as a multi-objective evolutionary algorithm in which the overall railyard condition is increased while decreasing cost to repair and maintain. A prioritization scheme for track maintenance is introduced that takes into account the volume of materials transported over the track and each rail segment’s primary purpose. Available repair options include repairing current 90 gauge rail, upgrade of rail segments to 115 gauge rail, and the swapping of rail removed during the upgrade. The proposed Multi-Objective Evolutionary Algorithm approach provides several advantages to the RAILER approach. The MOEA methodology allows decision makers to incorporate additional repair options beyond the current repair or do nothing options. It was found that many of the solutions identified by the evolutionary algorithm were both lower cost and provide a higher overall condition that those generated by DoD’s rail inventory and decision support system, RAILER. Additionally, the MOEA methodology generates lower cost, higher capability solutions when reduced sets of repair options are considered. The collection of non-dominated solutions provided by this technique gives decision makers increased flexibility and the ability to evaluate whether an additional cost repair solution is worth the increase in facility rail condition.

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“…MANETs speak to the first thought of the multihop worldview [1]. That is, the foundation of an impromptu system shaped by remote gadgets without requiring a focal framework.…”

Section: Manetsmentioning

confidence: 99%

“…Portable remote multihop impromptu systems have pulled in the consideration of mainstream researchers for over two decades [1] [4]. The fundamental thought of conveying electronic gadgets utilizing a remote multihop way, without the need of a focal framework or a settled foundation, has developed since its source and it is as yet a dynamic focal point of research.…”

Section: Introductionmentioning

confidence: 99%

Behavior of Manets with Evolutionary Algorithms

Bhattacharyya¹

2019

IJDCASD

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Developmental calculations are metaheuristic calculations that give semi ideal arrangements in a sensible time. They have been connected to numerous enhancement issues in a high number of logical regions. In this paper, we center on the utilization of developmental calculations to take care of improvement issues identified with a kind of complex system like portable multihop specially appointed systems. Since its starting point, portable multihop specially appointed system has advanced causing new kinds of multihop systems to seem, for example, vehicular impromptu systems and postpone tolerant systems, prompting the arrangement of new issues and streamlining issues. In this article, an audit of the primary work introduced for each kind of versatile multihop specially appointed system and additionally exhibit some imaginative thoughts and open difficulties to manage additionally explore in this point.

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A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems

Cited by 30 publications

References 84 publications

Artificial intelligence and game theory controlled autonomous UAV swarms

Artificial intelligence and game theory controlled autonomous UAV swarms

A Pareto Based Multi-Objective Evolutionary Algorithm Approach to Military Installation Rail Infrastructure Investment

Behavior of Manets with Evolutionary Algorithms

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