Application of Ant Colony Optimization in Cloud Computing Load Balancing

Wu, Zufa

doi:10.1007/978-3-319-68121-4_43

Cited by 1 publication

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“…It works on the interactions between ants via pheromones to find short paths between nests and food. ACO performs well for solving complex discrete optimization problems (Eldem & Ulker, 2017;Song & Gao, 2010;Wu, 2017). It starts by constructing the ants and move them in the search space according to state transition probability and pheromone.…”

Section: Aco-ant Colony Optimization Algorithmmentioning

confidence: 99%

Evolutionary Optimization for Prioritized Materialized View Selection

Madaan

Gosain

2022

International Journal of Information Retrieval Research

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Selecting appropriate views that provide faster query response time is a critical decision in data warehouse design. Top-level users expect quick results from a data warehouse for faster decision-making to gain a competitive edge in business. Prioritizing a view can distinguish views required to answer top-level users’ queries from regular users and provide a better selection chance. The prioritized materialized view selection (PMVS) problem addresses how to utilize the given space to materialize prioritized views more relevant to users. Particle swarm optimization algorithm has been used to achieve minimized query processing costs. Evolutionary algorithms are widely known to solve complex optimization problems quickly by reaching a semi-optimal solution. This paper explores the performance of six evolutionary algorithms: particle swarm optimization, coral reef optimization, cuckoo search, ant colony optimization, grey wolf optimization, and artificial bee colony. The results of empirical and statistical analysis show that PSO, CRO, and GWO algorithms are best suited to solve PMVS.

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Section: Aco-ant Colony Optimization Algorithmmentioning

confidence: 99%