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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

116
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...
116
Flame Photometry: Overview01:02

Flame Photometry: Overview

872
Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
872
Flame Photometry: Lab01:16

Flame Photometry: Lab

441
In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
441
Thermal expansion and Thermal stress: Problem Solving01:27

Thermal expansion and Thermal stress: Problem Solving

1.4K
San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in...
1.4K
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

201
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
201
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.8K

You might also read

Related Articles

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

Sort by
Same author

Improving the value of population health data for health policy and decision-making using machine learning algorithms in EQ-5D-5L index estimation.

Scientific reports·2026
Same author

Evolutionary salp swarm algorithm with multi-search strategies and advanced memory mechanism for solving global optimization and complex engineering problems.

Scientific reports·2025
Same author

Correction: Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care.

Journal of translational medicine·2025
Same author

Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performance.

Scientific reports·2025
Same author

Hybrid Adaptive Crayfish Optimization with Differential Evolution for Color Multi-Threshold Image Segmentation.

Biomimetics (Basel, Switzerland)·2025
Same author

A new intelligent control strategy for CSTH temperature regulation based on the starfish optimization algorithm.

Scientific reports·2025

Related Experiment Video

Updated: Oct 9, 2025

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.3K

An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering

Mohammad H Nadimi-Shahraki1,2, Ali Fatahi1,2, Hoda Zamani1,2

  • 1Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran.

Entropy (Basel, Switzerland)
|December 24, 2021
PubMed
Summary

An improved moth-flame optimization (I-MFO) algorithm enhances global optimization by addressing premature convergence and local optima entrapment. This novel approach improves search efficiency and solution quality for complex problems.

Keywords:
mechanical engineering problemsmetaheuristic algorithmsmoth-flame optimizationoptimizationswarm intelligence algorithm

More Related Videos

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K
Parametric Optimization Design Method for Friction Plates of Hydro-Viscous Clutches
10:58

Parametric Optimization Design Method for Friction Plates of Hydro-Viscous Clutches

Published on: July 22, 2025

202

Related Experiment Videos

Last Updated: Oct 9, 2025

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.3K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K
Parametric Optimization Design Method for Friction Plates of Hydro-Viscous Clutches
10:58

Parametric Optimization Design Method for Friction Plates of Hydro-Viscous Clutches

Published on: July 22, 2025

202

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The standard Moth-Flame Optimization (MFO) algorithm, inspired by moth navigation, effectively solves global optimization problems.
  • Canonical MFO faces challenges including premature convergence, reduced population diversity, local optima entrapment, and an exploration-exploitation imbalance.

Purpose of the Study:

  • To introduce an Improved Moth-Flame Optimization (I-MFO) algorithm designed to overcome the limitations of the standard MFO.
  • To enhance the MFO algorithm's ability to escape local optima and improve overall search performance.

Main Methods:

  • The I-MFO algorithm incorporates a memory mechanism for each moth to identify and escape local optima.
  • An Adapted Wandering Around Search (AWAS) strategy is introduced to facilitate the escape from suboptimal regions.
  • The proposed I-MFO algorithm's performance is validated using the CEC 2018 benchmark functions and compared against established metaheuristic algorithms.

Main Results:

  • The I-MFO algorithm demonstrated superior performance compared to other metaheuristic algorithms on the CEC 2018 benchmark functions.
  • Statistical analysis using the Friedman test across 30, 50, and 100 dimensions confirmed the significant effectiveness of I-MFO.
  • The I-MFO algorithm successfully applied to solve complex mechanical engineering problems from the CEC 2020 test suite, achieving optimal solutions.

Conclusions:

  • The proposed I-MFO algorithm significantly outperforms canonical MFO and other contender algorithms in solving global optimization problems.
  • I-MFO effectively addresses the shortcomings of the standard MFO, particularly in escaping local optima and maintaining population diversity.
  • The enhanced algorithm shows strong potential for application in complex engineering optimization tasks.