A novel binomial strategy for simultaneous topology and size optimization of truss structures

Mortazavi, Ali

doi:10.1080/0305215x.2024.2349100

Engineering Optimization

2024

DOI: 10.1080/0305215x.2024.2349100

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A novel binomial strategy for simultaneous topology and size optimization of truss structures

Ali Mortazavi

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2024

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Cited by 3 publications

References 47 publications

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A novel type-2 decision mechanism for dynamic parameter adaptation: theory and application in mathematical and structural problems

Mortazavi

2024

Neural Comput & Applic

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Metaheuristic algorithms are stochastic-based search techniques widely used for solving different types of optimization problems. These methods mostly adjust their search behavior using pre-defined search pattern(s) regardless of the current problem specifications. Therefore, integrating them with logical auxiliary modules can significantly enhance their search efficiency by enabling them to dynamically adapt their search behavior. The present study introduces a novel decision-making approach that employs interval type-2 fuzzy logic to balance the search behavior during optimization process. The proposed approach, designed as a stand-alone module with the flexibility to be integrated into various algorithms, is incorporated into the Interactive Search Algorithm. The developed reinforced technique is named Type-2 Fuzzy Interactive Search Algorithm. Performance of the proposed method is tested on different unconstrained mathematical functions and constrained structural and mechanical optimization problems. The attained results are compared with standard ISA method and seven other metaheuristic techniques through a suite of numerical and statistical evaluations. Drawing from the obtained results, the integration of the type-2 fuzzy decision module significantly enhances the algorithm's search capability. This improvement is evident in terms of stability, accuracy, and computational cost. It is worth noting that the employed comparative performance index for the proposed method indicates improvements of 3.38, 13.09, 16.61, and 27.23 percent compared to the best solution found by the selected methods for engineering problems.

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A novel type-2 decision mechanism for dynamic parameter adaptation: theory and application in mathematical and structural problems

Mortazavi

2024

Neural Comput & Applic

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Simultaneous optimization of capacity and topology of seismic isolation systems in multi-story buildings using a fuzzy reinforced differential evolution method

Mortazavi,

Kandemir

2024

Advances in Engineering Software

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Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks

Nalinipriya,

Lydia,

Sree

et al. 2024

Sci Rep

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Federated learning (FL) stimulates distributed on-device computation systems to process an optimum technique efficiency by communicating local process upgrades and global method distribution from aggregation averaging procedure. On-device FL is a standard application in wireless environments, with several mobile devices participating as nodes in the FL network. Managing extensive multi-dimensional process upgrades and resource-constrained computations in large-scale heterogeneous IoT cellular networks can be challenging. This article introduces a Lifetime Maximization using Optimal Directed Acyclic Graph Federated Learning in IoT Communication Networks (LM-ODAGFL) technique. The proposed LM-ODAGFL technique utilizes FL and metaheuristic optimization algorithms for energy-effective IoT networks. The Direct Acyclic Graph (DAG) model addresses device asynchrony in FL while minimizing additional resource usage. The Archimedes Optimization Algorithm (AOA) is designed to optimize the DAG model by reducing both user energy consumption and the training loss of the FL model. The performance validation of the LM-ODAGFL technique is performed by utilizing a series of experimentations. The obtained results of the LM-ODAGFL model demonstrate superior performance by consuming significantly less energy than SDAGFL and ESDAGFL, with values ranging from 0.373 to 0.485 kJ per round on the FMNIST-Clustered dataset and 16.27 to 20.34 kJ per round on the Poets dataset, compared to 0.000 to 1.442 kJ and 0.00 to 63.89 kJ respectively.

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A novel binomial strategy for simultaneous topology and size optimization of truss structures

Cited by 3 publications

References 47 publications

A novel type-2 decision mechanism for dynamic parameter adaptation: theory and application in mathematical and structural problems

A novel type-2 decision mechanism for dynamic parameter adaptation: theory and application in mathematical and structural problems

Simultaneous optimization of capacity and topology of seismic isolation systems in multi-story buildings using a fuzzy reinforced differential evolution method

Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks

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