Swarm Intelligence

Beni, G.

doi:10.1007/978-0-387-30440-3_530

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Swarm intelligence (SI), a novel distributed approach, may also readily handle optimization issues. Gerardo Beni and Jing Wang [25] created the notion of SI, which was influenced by natural phenomena such as bird flocking, ant colonies, animal swarming, fish schooling, and bacterial development. Based on biological events or processes, an attempt was made to build a different algorithm or dispersed problem-solving devices.…”

Section: Comparative Analysismentioning

confidence: 99%

A Multi-Goal Particle Swarm Optimizer for Test Case Prioritization

Nazir,

Mehmood,

Aslam

et al. 2023

IEEE Access

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Regression testing is carried out to test the updated supply code within the constraints of time and sources. Since it is very difficult to run all the updated source code every time, test case prioritization is needed to decrease the fee of regression testing. Various methodologies including extensions of white box and black box prioritization, have been presented considering the prioritization of test instances. In this context, the employment of particle swarm optimization (PSO) is usually recommended for test case prioritization. Single test case prioritization focuses to order test cases to maximize objectives like fault detection rate, execution time, etc. Regression testing for single-objective test suite prioritization can become challenging due to its longer execution time. However, test case prioritization for multi-objective functions is a complex and time-consuming task. A check suite may be organized in a certain order by an appropriate technique, subsequently permitting the detection of flaws as early as possible. Multi-goal particle swarm optimization (MOPSO) is used for case prioritization in regression testing. The purpose of MOPSO in this context is to organize the test suite in a specific order that maximizes fault coverage, provides sufficient coverage of test cases, and minimizes execution time. This study proposes an approach based on MOPSO that focuses on maximum fault coverage, most circumstance insurance, and minimal execution time. Experiments are performed using the average percentage of faults detected (APFD) to evaluate its performance. Performance analysis using APFD consisting of no order, opposite order, and random order indicates that the MOPSO surpasses all the previous techniques and obtains an 85% fault coverage. Moreover, MOPSO is better in terms of execution time, fault detection fee, and early detection capabilities.

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Section: Comparative Analysismentioning

confidence: 99%

A Multi-Goal Particle Swarm Optimizer for Test Case Prioritization

Nazir,

Mehmood,

Aslam

et al. 2023

IEEE Access

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Modeling of Human Behavior Within the Paradigm of Modern Physics

Lubashevsky

2017

Understanding Complex Systems

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Optimized Autofluorescence Spectral Signature for Non-Invasive Diagnostics of Ocular Surface Squamous Neoplasia (OSSN)

et al. 2019

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Clinical OSSN diagnostics by non-invasive spectral imaging of eye autofluorescence must be rapid enough to be comfortable for patients -without compromising accuracy. This requires identifying optimized spectral signatures of OSSN based on a minimal number of spectrally defined images. Here, we identified such signatures using a data-driven methodology of swarm intelligence. Ten patients with histopathological diagnosis of ocular surface squamous neoplasia (OSSN) were recruited. Their unstained biopsy OSSN specimens were investigated using a custom-built autofluorescence multispectral microscopy imaging system. The images were taken in 38 spectral channels spanning specific excitation (340 nm-510 nm) and emission (420 nm-650 nm) wavelength ranges. To identify optimized spectral signatures of OSSN from a small number of channels, swarm intelligence was combined with discriminative cluster analysis. This study established an optimized spectral signature of OSSN derived from multispectral data taken in 38 channels. Depending on the critical nature of the application and the consequences of misclassification error, two optimized spectral signatures with 5 and 10 channels were obtained which reduced the imaging time to 20 and 40 seconds, a reduction by 75% and 80 %, respectively. The K-nearest neighbor classifier was then built using OSSN spectral signatures and optimized to successfully detect OSSN with ∼1% and ∼14% misclassification error using 10 and 5 channels, respectively. Our study found an optimized spectral signature of OSSN allowing rapid diagnostic imaging in clinical settings and demonstrates the feasibility of using optimized multispectral autofluorescence spectral signatures to detect and determine boundaries of OSSN.

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Swarm Intelligence

Cited by 3 publications

References 34 publications

A Multi-Goal Particle Swarm Optimizer for Test Case Prioritization

A Multi-Goal Particle Swarm Optimizer for Test Case Prioritization

Modeling of Human Behavior Within the Paradigm of Modern Physics

Optimized Autofluorescence Spectral Signature for Non-Invasive Diagnostics of Ocular Surface Squamous Neoplasia (OSSN)

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