Artificial Bee Colony Optimization-Based Load Balancing in Distributed Computing Systems—A Survey

Handur, Vidya S.; Deshpande, Santosh L.

doi:10.1007/978-981-16-9967-2_69

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…The particle swarm optimization method [12] is an early intelligent algorithm that has the advantages of requiring few tuning parameters, simple principle and easy implementation [13]. Each particle in the population updates its location by individual best extreme (pbest) and neighborhood best extreme (nbest) to guide the particle to fly to a better area and get a better location.…”

Section: Particle Swarm Algorithmmentioning

confidence: 99%

“…In order to improve the diversity in the objective space, this paper proposes a selection strategy based on multivariate gray prediction, using the non-dominated special crowding distance [13] as the density measure for pbest i and nbest i , and keeping the two individuals that are closer in the objective space but farther away in the decision space, maintaining the diversity in the decision space. The selection strategy proposed in this paper is shown in Algorithm 2.…”

Section: Selection Strategymentioning

confidence: 99%

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A Multimodal Multi-Objective Evolutionary Particle Swarm Algorithm Based on Grey Prediction Selection Strategy

Liu,

Wang,

Yao

et al. 2023

Fuzzy Systems and Data Mining IX

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In view of the fact that the diversity of the objective space of multimodal multi-objective optimization will decrease with the increase of the diversity of the decision space, and it is unable to identify all pareto optimal solution sets, this paper proposes an evolutionary particle swarm optimization algorithm based on the gray prediction selection strategy to solve this problem. First, the historical optimal individuals and the neighborhood optimal individuals are selected by using multivariable gray prediction to improve the diversity of the target space; Secondly, the index based ring topology is used to update the neighborhood optimal archive, induce a stable ecological niche, and identify more pareto optimal solution sets; Finally, the non dominated special crowding distance is used as a density measure in the decision space and objective space to identify more pareto optimal solutions. In the experimental stage, 11 MMO test functions are selected and compared with four classic multimodal optimization algorithms. The results show that the proposed algorithm can identify all pareto optimal solution sets in the decision space and maintain good convergence and diversity in the objective space.

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Section: Particle Swarm Algorithmmentioning

confidence: 99%

Section: Selection Strategymentioning

confidence: 99%

A Multimodal Multi-Objective Evolutionary Particle Swarm Algorithm Based on Grey Prediction Selection Strategy

Liu,

Wang,

Yao

et al. 2023

Fuzzy Systems and Data Mining IX

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“…Among the multiple available paths in multipath routing protocols, balanced traffic distribution plays a critical role in determining network performance. Meta heuristic-based algorithm such as artificial bee colony optimization algorithm [12] has been proposed for the same purpose in distributed computer networks. The path reservation multipath routing (PRMR) [13] protocol has provided a path discovery mechanism along with a packet scheduling algorithm for every available traffic depending upon the service requirements.…”

mentioning

confidence: 99%

PRS-LD: priority-based rank selection load distribution protocol for routing in MANETs

2022

IJATEE

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Mobile ad hoc networks (MANETs) are complex distributed network systems where nodes are selforganized & self-configured, move freely and dynamically forming temporary or arbitrary ad hoc network topologies. It has wide applications for communication in military and rescue operations. These networks behave like a supplementary form of network for the internet of things (IoT) and fifthgeneration (5G) networks. Routing is a major functionality of the network that generates paths for traveling data packets across the network. The characteristics and nature of MANETs make traditional routing protocols impractical for ad hoc networks [1].*Author for correspondence Single path routing protocols have been introduced in the literature [2, 3] under two main categories named proactive and reactive routing protocols. Proactive routing such as destination sequenced distance vector (DSDV) [4] routing protocol consumes more energy because of large routing overhead caused due to maintenance of up-to-date information in routing tables of the node in advance, whereas reactive routing protocols such as ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) suffer from route discovery latencies arising due to the execution of route discovery phase whenever route request (RREQ) is initiated. In a view of resolving the above issues, multipath routing protocols [5] were introduced. Multipath variants of on-demand MANETs routing protocols such as ad hoc on-demand multipath distance vector (AOMDV)

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A Model-Based Machine Learning Approach for Assessing the Performance of Blockchain Applications

Albshri,

Alzubaidi,

Solaiman

2023

2023 IEEE International Conference on Smart Internet of Things (SmartIoT)

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Artificial Bee Colony Optimization-Based Load Balancing in Distributed Computing Systems—A Survey

Cited by 3 publications

References 14 publications

A Multimodal Multi-Objective Evolutionary Particle Swarm Algorithm Based on Grey Prediction Selection Strategy

A Multimodal Multi-Objective Evolutionary Particle Swarm Algorithm Based on Grey Prediction Selection Strategy

PRS-LD: priority-based rank selection load distribution protocol for routing in MANETs

A Model-Based Machine Learning Approach for Assessing the Performance of Blockchain Applications

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