In response to the shortcomings of dwarfmongooseoptimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as ***...
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In response to the shortcomings of dwarfmongooseoptimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as ***,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local *** addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in *** results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test ***,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization *** simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.
The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexi...
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The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with dwarfmongoosealgorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then dwarfmongoosealgorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively.
This study investigates the feasibility of relevance vector machine tuned with dwarf mongoose optimization algorithm in modeling monthly streamflow. The proposed method is compared with relevance vector machines tuned...
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This study investigates the feasibility of relevance vector machine tuned with dwarf mongoose optimization algorithm in modeling monthly streamflow. The proposed method is compared with relevance vector machines tuned by particle swarm optimization, whale optimization, marine predators algorithms, and single relevance vector machine methods. Various lagged values of hydroclimatic data (e.g., precipitation, temperature, and streamflow) are used as inputs to the models. The relevance vector machine tuned with dwarf mongoose optimization algorithm improved the efficiency of single method in monthly streamflow prediction. It is found that the integrating metaheuristic algorithms into single relevance vector machine improves the prediction efficiency, and among the input combinations, the lagged streamflow data are found to be the most effective variable on current streamflow whereas precipitation has the least effect.
Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited *** is widely utilized by future Internet of Things(IoT)*** a promising technology,Cognitive Rad...
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Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited *** is widely utilized by future Internet of Things(IoT)*** a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity ***,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy *** clusterrelated techniques for overall network management can be suitable for the scalability and stability of the *** paper focuses on designing the Modified dwarfmongooseoptimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for *** MDMO-EAC technique mainly intends to group the nodes into clusters in the ***,theMDMOEAC algorithm is based on the dwarfmongooseoptimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the *** addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network *** presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying *** comparison study stated the improvements of the MDMO-EAC method over other recent approaches.
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