A comparative study of three new parallel models based on the PSO algorithm

Zemzami, Maria; Hami, Norelislam El; Itmi, Mhamed; Hmina, Nabil

doi:10.1051/smdo/2019022

Cited by 8 publications

(8 citation statements)

References 8 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each particle represents a solution in the search space and moves based on its own experience and the experience of the swarm in general. The goal is to find the best possible solution to an optimization problem [21,22].…”

Section: Particle Swarm Optimization and Gray Wolf Optimizermentioning

confidence: 99%

Optimizing EMG Classification through Metaheuristic Algorithms

2023

View full text Add to dashboard Cite

This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and the gray wolf optimizer significantly improves the performance of a multilayer perceptron in classifying EMG motion signals. The final model achieves an average classification rate of 93% for the validation phase. The results obtained are promising and suggest that the proposed approach may be helpful for the optimization of deep learning models in other signal processing applications.

show abstract

Section: Particle Swarm Optimization and Gray Wolf Optimizermentioning

confidence: 99%

Optimizing EMG Classification through Metaheuristic Algorithms

2023

View full text Add to dashboard Cite

show abstract

“…Also, PSO prioritization helps in improving the test cases execution time by considering selected set of test cases and eliminating the irrelevant set of test cases in turn helping by improving the code coverage information as well [18,20,29,30]. One more interesting feature of PSO algorithm is that it helps in generating improvised APFD results when compared with random ordering [31][32][33]. PSO algorithm has a capability of improving fault detection but does not focus much on the PSO performance when compared with the SI algorithms in TSR [7,14,20,26].…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Geetha¹,

Sankar²

2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Software testing plays a pivotal role in entire software development lifecycle. It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required. These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite, fault-tolerance, defects due to uncovered-statements and overall-performance at the time of execution. With this objective, the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization. The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate (90.78% to 100%) and failure detection rate (44.56% to 55.01%). Results proved MOMPSO outperformed the other stated algorithms.

show abstract

“…Although the PSO-based node localization approach [31] is computationally effective, there is not much improvement in the localization error. Bat algorithm-based localization [32] replicates bats' behavior using echolocation for the prey hunting during the darkness.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

et al. 2021

View full text Add to dashboard Cite

An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process's continuance and adding proactive and reactive features. The proposed algorithm's performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application.

show abstract

A comparative study of three new parallel models based on the PSO algorithm

Cited by 8 publications

References 8 publications

Optimizing EMG Classification through Metaheuristic Algorithms

Optimizing EMG Classification through Metaheuristic Algorithms

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

Contact Info

Product

Resources

About