A new optimization algorithm based on mimicking the voting process for leader selection

PeerJ Computer Science, Journal Year: 2022, Volume and Issue: 8, P. e976 - e976

Published: May 13, 2022

Stochastic-based optimization algorithms are effective approaches to addressing challenges. In this article, a new algorithm called the Election-Based Optimization Algorithm (EBOA) was developed that mimics voting process select leader. The fundamental inspiration of EBOA process, selection leader, and impact public awareness level on population is guided by search space under guidance elected EBOA’s mathematically modeled in two phases: exploration exploitation. efficiency has been investigated solving thirty-three objective functions variety unimodal, high-dimensional multimodal, fixed-dimensional CEC 2019 types. implementation results show its high ability global search, exploitation local as well strike proper balance between which led proposed approach optimizing providing appropriate solutions. Our analysis shows provides an and, therefore, better more competitive performance than ten other it compared.

Language: Английский

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Cancer classification in high dimensional microarray gene expressions by feature selection using eagle prey optimization

Frontiers in Genetics, Journal Year: 2025, Volume and Issue: 16

Published: March 21, 2025

Microarray gene expression data have emerged as powerful tools in cancer classification and diagnosis. However, the high dimensionality of these datasets presents significant challenges for feature selection, leading to development various computational methods. In this paper, we utilized Eagle Prey Optimization (EPO), a novel genetically inspired approach microarray selection classification. EPO draws inspiration from remarkable hunting strategies eagles, which exhibit unparalleled precision efficiency capturing prey. Similarly, our algorithm aims identify small subset informative genes that can discriminate between subtypes with accuracy minimal redundancy. To achieve this, employs combination genetic mutation operator fitness function, evolve population potential subsets over multiple generations. The key innovation lies its incorporation function specifically designed tasks. This considers not only discriminative power selected but also their diversity redundancy, ensuring creation compact subsets. Moreover, incorporates mechanism adaptive rates, allowing explore search space efficiently. validate effectiveness EPO, extensive experiments were conducted on several publicly available representing different types. Comparative analysis state-of-the-art algorithms demonstrates consistently outperforms methods terms accuracy, reduction, robustness noise.

Language: Английский

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Role of Cloud Computing in Improve the Healthcare System

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 133 - 154

Published: April 17, 2025

In medical care cloud computing helps the digital Cloud ecosystem of care. Currently, requires better data management to improve cooperation among service providers and optimize everyday activities. calculations are attractive in health market. The allows you increase innovation general allow IoT solutions for services contribute collection analysis real time patient monitoring. healthcare, actual application shows variant potential. Using AI diagnosis robot operation, these technologies management, use power results.

Language: Английский

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A Comprehensive Review of Cloud Computing Virtual Machine Consolidation

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 106190 - 106209

Published: Jan. 1, 2023

In the last decade, users can access their applications, data, and services via cloud from any location with an internet connection. The scale of heterogeneous environments is continuously growing due to development computing-intensive smart devices. A data center central processing unit environment, it made up hardware-oriented machines known as Physical Machines (PMs) or server software-oriented Virtual (VMs). deployment a huge number physical servers result exponential in demand for has resulted high energy consumption ineffective resource usage. Efficient utilization minimizing power by have become crucial challenges. machine consolidation(VMC) method optimizing computing resources consolidating multiple VMs onto reduced PMs. By running fewer servers, VM consolidation lead reducing efficient utilization. This review paper presents comprehensive analysis virtual consolidation, exploring various strategies, benefits, challenges, future trends this domain. examining wide range literature year 2015 2023, attempts provide insight into current state its possible effects on performance sustainability computing. main flaw articles that authors focused different assessment metrics while emphasis should been improving efficiency quality service systems. Future research be aimed at developing multi-objective system emphasizes usage without sacrificing quality, preventing level agreements being compromised.

Language: Английский

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MOM-VMP: multi-objective mayfly optimization algorithm for VM placement supported by principal component analysis (PCA) in cloud data center

Cluster Computing, Journal Year: 2023, Volume and Issue: 27(2), P. 1733 - 1751

Published: June 5, 2023

Language: Английский

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A Fuzzy Grouping Genetic Algorithm for Solving a Real-World Virtual Machine Placement Problem in a Healthcare-Cloud

Algorithms, Journal Year: 2022, Volume and Issue: 15(4), P. 128 - 128

Published: April 14, 2022

Due to the large-scale development of cloud computing, data center electricity energy costs have increased rapidly. Energy saving has become a major research direction virtual machine placement problems. At same time, multi-dimensional resources on should be used in balanced manner order avoid waste. In this context, paper addresses real-world problem arising Healthcare-Cloud (H-Cloud) hospitals chain Saudi Arabia, considering server power consumption and resource utilization. As part optimizing both objectives, user service quality taken into account. fact, (QoS) is also considered by measuring Service-Level Agreement (SLA) violation rate. This modeled as multi-objective with objective minimizing consumption, utilization, SLA To solve challenging problem, fuzzy grouping genetic algorithm (FGGA) proposed. Considering that multiple optimization objectives may different degrees influence fitness function proposed calculated logic-based function. The experimental results show effectiveness algorithm.

Language: Английский

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Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform

PeerJ Computer Science, Journal Year: 2023, Volume and Issue: 9, P. e1675 - e1675

Published: Nov. 14, 2023

Virtual machine scheduling and resource allocation mechanism in the process of dynamic virtual consolidation is a promising access to alleviate cloud data centers prominent energy consumption service level agreement violations with improvement quality (QoS). In this article, we propose an efficient algorithm (AESVMP) based on Analytic Hierarchy Process (AHP) for accordance measure. Firstly, take into consideration three key criteria including host power consumption, available balance ratio, which ratio can be calculated by value between overall three-dimensional (CPU, RAM, BW) flat surface (when new migrated (VM) consumed targeted host's resource). Then, placement decision determined application multi-criteria making techniques AHP embedded above-mentioned criteria. Extensive experimental results CloudSim emulator using 10 PlanetLab workloads demonstrate that proposed approach reduce center number migration, violation (SLAV), aggregate indicators comsumption (ESV) average 51.76%, 67.4%, 67.6% compared cutting-edge method LBVMP, validates effectiveness.

Language: Английский

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Multi-Objective Reinforcement Learning for Virtual Machines Placement in Cloud Computing

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(3)

Published: Jan. 1, 2024

The rapid demand for cloud services has provoked providers to efficiently resolve the problem of Virtual Machines Placement in cloud. This paper presents a VM using Reinforcement Learning that aims provide optimal resource and energy management data centers. provides better decision-making as it solves complexity caused due tradeoff among objectives hence is useful mapping requested on minimum number Physical Machines. An enhanced Tournament-based selection strategy along with Roulette Wheel sampling been applied ensure optimization goes through balanced exploration exploitation, thereby giving solution quality. Two heuristics have used ordering VM, considering impact CPU memory utilizations over placement. Moreover, concept Pareto approximate set considered both are prioritized according perspective users. proposed technique implemented MATLAB 2020b. Simulation analysis showed VMRL performed preferably well shown improvement 17%, 20% 18% terms consumption, utilization fragmentation respectively comparison other multi-objective algorithms.

Language: Английский

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Multi-Objective Optimization for Optimal VM Allocation using Tuna Swarm Optimization

Published: March 1, 2024

Limitations of physical machines (PMs) and hence computation power are well known to humans today, due which virtual (VMs) used enhance the capability reduce cost space consumption for hardware. To leverage these VMs optimally, placement into PMs or clusters needs be worked upon. Previously people have towards this allocation task using various approaches such as nature-inspired algorithms, decision-based ML-driven etc. Over years, new algorithms constantly being created quality optimization at hand. In paper, we compared Tuna Swarm Optimization with previously implemented techniques multi-objective in VMs.

Language: Английский

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Optimizing Cloud Computing Energy Efficiency with a Grasshopper-Inspired Technique for Virtual Machine Migration

Published: June 21, 2024

Language: Английский

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