Numerical Comparison of the Performance of Genetic Algorithm and Particle Swarm Optimization in Excavations

Hashemi, Seyyed Mohammad Reza; Rahmani, Iraj

doi:10.28991/cej-03091149

Cited by 17 publications

(1 citation statement)

References 8 publications

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“…In this paper, we also used a randomly generated task graph to compare the performance of the algorithm [12,31]. The running time of our proposed algorithm OIWHO is compared with the five most advanced algorithms, QHA, PSO, GWO, LFD and OIWOA [32].…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Research and optimization of task schedulingalgorithm based on heterogeneous multi-coreprocessor

Liu,

Ding

2024

Preprint

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Heterogeneous multi-core processor has the ability to switch between differenttypes of cores to perform tasks, which provides more space and possibility forrealizing efficient operation of computer system and improving computer computing power. Current research focuses on heterogeneous multiprocessor systemswith high performance or low power consumption to reduce system energy consumption. However, some studies have shown that excessive voltage reductionmay lead to an increase in transient failure rates, reducing system reliability. Inorder to solve this problem, this paper studies the energy optimal schedulingproblem of HMSS with DVFS under the constraints of minimum time and reliability, and proposes an improved wild horse optimization algorithm (OIWHO),which improves the efficiency of heterogeneous task scheduling and shortens thetask completion time. The algorithm uses the learning and chaos perturbationstrategies based on opposition and crossover strategies to balance the search andutilization capabilities, and can further improve the performance of OIWHO.Compared with previous work, our proposed algorithm has more advantagesthan existing algorithms. Experimental results show that the average computingtime of OIWHO algorithm is 13.38%, 10.90%, 6.97%, 2.39% and 3.21% fasterthan QHA, PSO, GWO, LFD and OIWOA, respectively. Especially when solvinglarge-scale problems, our algorithm takes less time than other algorithms.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Research and optimization of task schedulingalgorithm based on heterogeneous multi-coreprocessor

Liu,

Ding

2024

Preprint

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An efficient optimization approach for designing machine learning models based on genetic algorithm

Hamdia

Zhuang

Rabczuk

2020

Neural Comput & Applic

197

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Machine learning (ML) methods have shown powerful performance in different application. Nonetheless, designing ML models remains a challenge and requires further research as most procedures adopt a trial and error strategy. In this study, we present a methodology to optimize the architecture and the feature configurations of ML models considering a supervised learning process. The proposed approach employs genetic algorithm (GA)-based integer-valued optimization for two ML models, namely deep neural networks (DNN) and adaptive neuro-fuzzy inference system (ANFIS). The selected variables in the DNN optimization problems are the number of hidden layers, their number of neurons and their activation function, while the type and the number of membership functions are the design variables in the ANFIS optimization problem. The mean squared error (MSE) between the predictions and the target outputs is minimized as the optimization fitness function. The proposed scheme is validated through a case study of computational material design. We apply the method to predict the fracture energy of polymer/nanoparticles composites (PNCs) with a database gathered from the literature. The optimized DNN model shows superior prediction accuracy compared to the classical one-hidden layer network. Also, it outperforms ANFIS with significantly lower number of generations in GA. The proposed method can be easily extended to optimize similar architecture properties of ML models in various complex systems.

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A new integrated model of the group method of data handling and the firefly algorithm (GMDH-FA): application to aeration modelling on spillways

Mahdavi‐Meymand

Zounemat‐Kermani

2019

Artif Intell Rev

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Numerical Comparison of the Performance of Genetic Algorithm and Particle Swarm Optimization in Excavations

Cited by 17 publications

References 8 publications

Research and optimization of task schedulingalgorithm based on heterogeneous multi-coreprocessor

Research and optimization of task schedulingalgorithm based on heterogeneous multi-coreprocessor

An efficient optimization approach for designing machine learning models based on genetic algorithm

A new integrated model of the group method of data handling and the firefly algorithm (GMDH-FA): application to aeration modelling on spillways

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