A robust energy efficient ant colony optimization routing algorithm for multi-hop ad hoc networks in MANETs

Vijayalakshmi, P.; Francis, Sharmila Anand John; Dinakaran, J. Abraham

doi:10.1007/s11276-015-1082-1

Cited by 21 publications

(10 citation statements)

References 19 publications

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“…They are not only adapted in V2V communications, but also associated with devices like Road Side Units (RST). J. Amudhavel et al [39] have introduced the idea of using recursive ant colony optimization. They divide routes into sub-routes, which contain one or more RSUs in each sub-set.…”

Section: Discussionmentioning

confidence: 99%

A Survey of Location Aware Ant Colony Optimization Routing Protocols in MANETs

Zhang¹,

Wang²,

Hogrefe³

2017

Proceedings of the 10th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BION

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Mobile Ad hoc NETtworks (MANETs) are infrastructureless and self-configuring networks which consist of wireless mobile devices. Due to these properties, routing in such networks is a challenging task. Developing highly efficient routing protocols for MANETs is an important issue, with solutions having to fulfill many requirements, such as being able to provide low packet delay, high packet delivery rate and effective adaption to network topology changes with low control overhead. Swarm intelligence inspired algorithms offer possible solutions that fulfill the requirements and so have attracted a lot of attention from academics. A successful example in the swarm intelligence category is the Ant Colony Optimization (ACO) algorithm which has been applied to balance the various routing related requirements in MANETs. This paper presents a survey which focuses on location aware ACO routing protocols in MANETs. The main contributions of this survey include 1) introducing the ACO routing principles, 2) surveying and comparing a selection of routing protocols from the perspective of design and simulation parameters, and 3) discussing open issues and future possible design directions of ACO based routing protocols.

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Section: Discussionmentioning

confidence: 99%

A Survey of Location Aware Ant Colony Optimization Routing Protocols in MANETs

Zhang¹,

Wang²,

Hogrefe³

2017

Proceedings of the 10th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BION

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“…Zhou et al [56] proposed a multi-objective multi-population ACO algorithm for continuous domain. Vijayalakshmi et al [57] proposed a novel robust energy efficient ACO routing algorithm to enhance the performance of Max-Min-Path approach. Tiwari and Vidyarthi [58] proposed an improved auto controlled ACO algorithm using the lazy ant concept.…”

Section: Related Workmentioning

confidence: 99%

An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem

2019

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In this paper, an improved ant colony optimization(ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability. INDEX TERMS Co-evolution mechanism, ACO, pheromone updating strategy, pheromone diffusion mechanism, hybrid strategy, assignment problem.

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“…The Ant Colony Optimization (ACO) is a pattern for designing metaheuristic algorithms based on real ants' behavior. These ants deposit pheromones on the ground to mark some favourable pathway that should be followed by other members of the colony (Duran Toksarı, 2007;Kadkhodaie-Ilkhchi, 2015;Vijayalakshmi et al, 2015;Zerafat et al, 2009). The ACO exploits a similar mechanism for solving optimization problems.…”

Section: Continuous Ant Colony Optimization Algorithm In Gas Lift Optmentioning

confidence: 99%

Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies

Okorocha

Chinwuko

Mgbemena

et al. 2020

Int. J. Ind. Optim.

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Gas Lift operation involves the injection of compressed gas into a low producing or non-performing well to maximize oil production. The oil produced from a gas lift well is a function of the gas injection rate. The optimal gas injection rate is achieved by optimization. However, the gas lift, which is an artificial lift process, has some drawbacks such as the deterioration of the oil well, incorrect production metering, instability of the gas compressor, and over injection of gas. This paper discusses the various optimization techniques for the gas lift in the Oil and Gas production process. A systematic literature search was conducted on four databases, namely Google Scholar, Scopus, IEE Explore and DOAJ, to identify papers that focused on Gas lift optimizations. The materials for this review were collected primarily via database searches. The major challenges associated with gas lift were identified, and the different optimization strategies available in the literature reviewed. The strategies reviewed were found to be based on artificial intelligence (AI) and machine learning (ML). The implementation of any of the optimization strategies for the gas lift will enhance profitability, reduce operational cost, and extend the life of the wells.

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A robust energy efficient ant colony optimization routing algorithm for multi-hop ad hoc networks in MANETs

Cited by 21 publications

References 19 publications

A Survey of Location Aware Ant Colony Optimization Routing Protocols in MANETs

A Survey of Location Aware Ant Colony Optimization Routing Protocols in MANETs

An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem

Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies

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