Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Optimal Foraging00:48

Optimal Foraging

14.1K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
14.1K
Optimization Problems01:26

Optimization Problems

103
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
103
Heuristics01:21

Heuristics

799
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
799
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.2K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

376
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
376
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.3K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Felis Catus Optimization (FCO): A novel nature‑inspired metaheuristic algorithm.

PloS one·2026
Same author

An Integrated Predictive Impact-Enhanced Process Mining Framework for Strategic Oncology Workflow Optimization: Case Study in Iran.

Bioengineering (Basel, Switzerland)·2025
Same author

Overview of Monitoring, Diagnostics, Aging Analysis, and Maintenance Strategies in High-Voltage AC/DC XLPE Cable Systems.

Sensors (Basel, Switzerland)·2025
Same author

The Application of Data Envelopment Analysis to Emergency Departments and Management of Emergency Conditions: A Narrative Review.

Healthcare (Basel, Switzerland)·2023
Same author

Dynamic Performance Assessment of Hospitals by Applying Credibility-Based Fuzzy Window Data Envelopment Analysis.

Healthcare (Basel, Switzerland)·2022
Same author

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic.

Bioengineering (Basel, Switzerland)·2022

Related Experiment Video

Updated: Mar 6, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K

A novel multi objective grey wolf optimization fuzzy miner for process discovery: Incorporating robustness and

Mohammad Salehi1, Rauof Khayami1, Mirpouya Mirmozaffari2

  • 1Computer Engineering and Information Technology Department, Shiraz University of Technology, Shiraz, Iran.

Plos One
|March 4, 2026
PubMed
Summary

Fuzzy Multi-Objective Grey Wolf Optimization (Fuzzy MOGWO) enhances process discovery by optimizing six metrics, including noise resilience and interpretability. This novel approach significantly outperforms existing methods in both noise-free and noisy environments.

More Related Videos

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

10.8K

Related Experiment Videos

Last Updated: Mar 6, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

10.8K

Area of Science:

  • Process mining
  • Artificial intelligence
  • Metaheuristic optimization

Background:

  • Process mining analyzes event logs to understand and improve business processes.
  • Existing methods often struggle with noise and lack interpretability.
  • There is a need for robust and explainable process discovery techniques.

Purpose of the Study:

  • Introduce Fuzzy Multi-Objective Grey Wolf Optimization (Fuzzy MOGWO) for process discovery.
  • Simultaneously optimize six key metrics: Fitness, Precision, Generalization, Simplicity, Robustness, and Explainability.
  • Evaluate Fuzzy MOGWO's performance against established process mining algorithms.

Main Methods:

  • Integration of fuzzy modeling with a multi-criteria metaheuristic optimization approach.
  • Development of a normalized scoring mechanism using the L₂ norm for balanced objective evaluation.
  • Benchmarking Fuzzy MOGWO against Alpha Miner, Inductive Miner, and Fuzzy Miner on synthetic and real-world event logs, including noisy datasets.

Main Results:

  • Fuzzy MOGWO achieved a normalized score of 0.329 in noise-free conditions, outperforming the best baseline by 14.24%.
  • In noisy environments, Fuzzy MOGWO scored 0.440, exceeding the top competitor by 16.40%.
  • On real-world logs, Fuzzy MOGWO outperformed competitors in 4 out of 6 metrics, demonstrating superior effectiveness and robustness.

Conclusions:

  • Fuzzy MOGWO offers a comprehensive and reliable solution for challenging process discovery tasks.
  • The proposed method exhibits substantially improved effectiveness, robust performance under noise, and enhanced interpretability.
  • Fuzzy MOGWO sets a new standard for multi-objective process discovery by balancing multiple critical performance dimensions.