Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Random Sampling Method

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...

Sampling Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

Systematic Sampling Method

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

Sampling Methods: Overview

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling.
In analytical chemistry, the choice of sampling...

You might also read

Related Articles

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

Sort by

Same author

Meteorological Visibility Estimation Using Landmark Object Extraction and the ANN Method.

Sensors (Basel, Switzerland)·2025

Same author

PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network.

Sensors (Basel, Switzerland)·2024

Same author

One-wire reconfigurable and damage-tolerant sensor matrix inspired by the auditory tonotopy.

Science advances·2023

Same author

Genetic Learning Particle Swarm Optimization.

IEEE transactions on cybernetics·2015

Same author

Optimal Selection of Parameters for Nonuniform Embedding of Chaotic Time Series Using Ant Colony Optimization.

IEEE transactions on cybernetics·2012

Same author

Adaptive particle swarm optimization.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2009

Same journal

Strategic Ability Updating in Concurrent Games by Coalitional Commitment.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2015

Same journal

Meta-Analysis of the First Facial Expression Recognition Challenge.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012

Same journal

Adjustable model-based fusion method for multispectral and panchromatic images.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012

Same journal

Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012

Same journal

A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012

Same journal

Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012

See all related articles

Search research articles

Related Experiment Videos

SamACO: variable sampling ant colony optimization algorithm for continuous optimization.

Xiao-Min Hu¹, Jun Zhang, Henry Shu-Hung Chung

¹Department of Computer Science, Sun Yat-Sen University, Guangzhou 510275, China.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|April 8, 2010

Summary

This summary is machine-generated.

This study introduces SamACO, a novel ant colony optimization (ACO) algorithm for continuous problems. SamACO effectively transforms discrete optimization techniques for continuous variable sampling, showing promising results.

Related Experiment Videos

Area of Science:

Computational Intelligence
Optimization Algorithms
Swarm Intelligence

Background:

Ant Colony Optimization (ACO) is primarily designed for discrete optimization problems.
Extending ACO to continuous optimization requires novel approaches for variable sampling and solution construction.

Purpose of the Study:

To present a new method for extending Ant Colony Optimization (ACO) to solve continuous optimization problems.
To introduce the SamACO algorithm, focusing on continuous variable sampling.

Main Methods:

The SamACO algorithm involves three key steps: candidate variable generation, ants' solution construction, and pheromone updates.
A novel sampling method discretizes the continuous search space, enabling efficient incremental solution construction.

Main Results:

SamACO demonstrated competitive performance on continuous numerical functions with unimodal and multimodal features.
The algorithm's effectiveness was validated against traditional ant-based and other computational intelligence algorithms.

Conclusions:

SamACO offers a promising extension of ACO for continuous optimization problems.
The proposed sampling and construction methods are key to transforming ACO for continuous domains.