Distributed meta-scheduling in lambda grids by means of Ant Colony Optimization

Pavani, Gustavo Sousa; Tinini, Rodrigo Izidoro

doi:10.1016/j.future.2016.04.005

Cited by 8 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several algorithms are used today, including PSO, ACO, bee colony optimization, and genetic algorithms. The ant-colony based route algorithm is one algorithm based on the ACO [21], HOPNET, AntHocNet [22], Ant-based Dynamic Zone Routing Protocol [23] and Hybrid ACO Routing [24] Foraging behaviour of ants is the basis for this theory [25].…”

Section: Literaturementioning

confidence: 99%

Design an Improved Trust-based Quality of Service AwareRouting in Cognitive Mobile Ad-Hoc Network

Dr. Alok Singh Chauhan,

H Mary Henrietta

2023

WJCMS

View full text Add to dashboard Cite

Mobile ad hoc networks (MANETs) are wireless networks that can be configured at will. It has no infrastructure and centralized control, so it is only suitable for provisional communications. In a dynamically topological and resource-constrained network, ensuring QoS and security is challenging. MANETs are dynamic networks, so navigating them can be challenging and more susceptible to attacks. MANET requires significant memory, speed, and transmission bandwidth for conventional security measures like cryptographic techniques. Consequently, these methods are unsuitable for identifying malicious behaviour or self-centered nodes. Nodes that are malicious, selfish, or malfunctioning can be identified based on the trust method, which calculates how much trust exists between them. A trust-based QOS-aware routing protocol is proposed in this paper to calculate trust in MANET (I-TQAR). The tree important performance metrics are considered for result validation such as delay, throughput and packet delivery ratio (PDR). I-TQAR offers significantly improved performance in all areas compared to the existing TQR and TQOR protocols.

show abstract

Section: Literaturementioning

confidence: 99%

Design an Improved Trust-based Quality of Service AwareRouting in Cognitive Mobile Ad-Hoc Network

Dr. Alok Singh Chauhan,

H Mary Henrietta

2023

WJCMS

View full text Add to dashboard Cite

show abstract

“…The core of the above methods is the GA and differences are primarily the selection mechanism and fitness evaluation [18]. Apart from the EA, swarm intelligence [19], which derived from the concept of the social and collective behavior, emerged recently and mainly includes Ant Colony Optimization (ACO) [20,21] and Particle Swarm Optimization (PSO) [22][23][24].…”

Section: Strength Pareto Evolutionary Algorithm (Spea)mentioning

confidence: 99%

A Novel Multiobjective Optimization Method Based on Sensitivity Analysis

Sun

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

For multiobjective optimization problems, different optimization variables have different influences on objectives, which implies that attention should be paid to the variables according to their sensitivity. However, previous optimization studies have not considered the variables sensitivity or conducted sensitivity analysis independent of optimization. In this paper, an integrated algorithm is proposed, which combines the optimization method SPEA (Strength Pareto Evolutionary Algorithm) with the sensitivity analysis method SRCC (Spearman Rank Correlation Coefficient). In the proposed algorithm, the optimization variables are worked as samples of sensitivity analysis, and the consequent sensitivity result is used to guide the optimization process by changing the evolutionary parameters. Three cases including a mathematical problem, an airship envelope optimization, and a truss topology optimization are used to demonstrate the computational efficiency of the integrated algorithm. The results showed that this algorithm is able to simultaneously achieve parameter sensitivity and a well-distributed Pareto optimal set, without increasing the computational time greatly in comparison with the SPEA method.

show abstract