Multi-objective routing optimization using evolutionary algorithms

Yetgin, Halil; Cheung, Kent Tsz Kan; Hanzo, Lajos

doi:10.1109/wcnc.2012.6214324

Cited by 43 publications

(63 citation statements)

References 14 publications

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“…Most current EA-based multi-objective routing optimisation approaches consider single-path routing schemes to optimise various objectives: energy efficiency, network lifetime, latency, robustness, expected transmission count, etc. [11,12,13,14]. To the best of our knowledge, this work represents the first attempt to estimate and explore the optimal trade-off between network lifetime and robustness using multi-path routings in wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks

2016

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Wireless sensor networks frequently use multi-path routing schemes between nodes and a base station. Multi-path routing confers additional robustness against link failure, but in batterypowered networks it is desirable to choose paths which maximise the overall network lifetime -the time at which a battery is first exhausted. We introduce multi-objective evolutionary algorithms to find the routings which approximate the optimal trade-off between network lifetime and robustness. A novel measure of network robustness, the fragility, is introduced. We show that the distribution of traffic between paths in a given multi-path scheme that optimises lifetime or fragility may be found by solving the appropriate linear program. A multi-objective evolutionary algorithm is used to solve the combinatorial optimisation problem of choosing routings and traffic distributions that give the optimal trade-off between network lifetime and robustness. Efficiency is achieved by pruning the search space using k-shortest paths, braided and edge disjoint paths. The method is demonstrated on synthetic networks and a real network deployed at the Victoria & Albert Museum, London. For these networks, using only two paths per node, we locate routings with lifetimes within 3% of those obtained with unlimited paths per node. In addition, routings which halve the network fragility are located. We also show that the evolutionary multi-path routing can achieve significant improvement in performance over a braided multi-path scheme.

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Section: Related Workmentioning

confidence: 99%

Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks

2016

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“…The idea is to find the most optimal parameters for the performance of a routing protocol. In [34], the authors test several multiobjective optimization algorithms to optimize a simple routing protocol that finds routes between two nodes in the network. They use Nondominated Sorting Based Genetic Algorithm-II (NSGA-II) and the Multiobjective Differential Evolution (MODE) to optimize energy cost and E2E delay performance metrics.…”

Section: Routing Protocolsmentioning

confidence: 99%

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

Reina

Ruiz

Ciobanu

et al. 2016

International Journal of Distributed Sensor Networks

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Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network like mobile multihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic.

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“…An important consideration with distributed approaches is that nodes require sufficient computational power and storage to collect and store information regarding local connectivity and compute the best routes based on available information. In comparison, centralised approaches, which mostly incorporate variants of heuristic algorithms (Chang and Tassiulas, 2004;Xue et al, 2006;Islam and Hussain, 2006;Yetgin et al, 2012) require lower computational power and storage at the nodes, as most of the computation and storage is conducted 2 Evolutionary Computation Volume x, Number x by the central base station. Nonetheless, there is a system-wide overhead incurred in gathering connectivity information and broadcasting routing information.…”

Section: Introductionmentioning

confidence: 99%

“…This is the case even for multi-objective routing optimisation, where energy expense is optimised with additional objectives describing different factors, such as quality of service, bandwidth, packet loss ratio, etc. (Xue et al, 2006;Yetgin et al, 2012). However, optimising the overall energy expenditure of a network may be detrimental to the overall performance of the network, because often the goal is to prolong the lifetime of the network before any battery needs replacing.…”

Section: Introductionmentioning

confidence: 99%

Hybrid evolutionary approaches to maximum lifetime routing and energy efficiency in sensor mesh networks

Rahat¹,

Everson²,

Fieldsend³

2015

SIGEVOlution

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Mesh network topologies are becoming increasingly popular in battery powered wireless sensor networks, primarily due to the extension of network range. However, multi-hop mesh networks suffer from higher energy costs, and the routing strategy employed directly affects the lifetime of nodes with limited energy resources. Hence when planning routes there are trade-offs to be considered between individual and system-wide battery lifetimes. We present a multi-objective routing optimisation approach using hybrid evolutionary algorithms to approximate the optimal trade-off between minimum lifetime and the average lifetime of nodes in the network. In order to accomplish this combinatorial optimisation rapidly, our approach prunes the search space using k-shortest path pruning and a graph reduction method which finds candidate routes promoting long minimum lifetimes. When arbitrarily many routes from a node to the base station are permitted, optimal routes may be found as the solution to a well-known linear program. We present an evolutionary algorithm that finds good routes when each node is allowed only a small number of paths to the base station. On a real network deployed in the Victoria & Albert Museum, London, these solutions, using only three paths per node, are able to achieve minimum lifetimes of over 99% of the optimum linear program solution's time to first sensor battery failure.

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Multi-objective routing optimization using evolutionary algorithms

Cited by 43 publications

References 14 publications

Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks

Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks

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

Hybrid evolutionary approaches to maximum lifetime routing and energy efficiency in sensor mesh networks

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