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

Observational Learning01:12

Observational Learning

1.2K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.2K
Principle of Linear Impulse and Momentum for a Single Particle: Problem Solving01:23

Principle of Linear Impulse and Momentum for a Single Particle: Problem Solving

1.2K
Consider a wooden box and a cylinder of known masses m1 and m2, respectively,  hanging from a ceiling with the help of a massless pulley system.
1.2K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

3.4K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
3.4K
Principle of Moments: Problem Solving01:30

Principle of Moments: Problem Solving

1.3K
The principle of moments is a fundamental concept in physics and engineering. It refers to the balancing of forces and moments around a point or axis, also known as the pivot. This principle is used in many real-life scenarios, including construction, sports, and daily activities like opening doors and pushing objects.
One such scenario involves a pole placed in a three-dimensional system with a cable attached. When a tension is applied to the cable, the moment about the z-axis passing through...
1.3K
Cognitive Learning01:21

Cognitive Learning

1.5K
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
1.5K
Associative Learning01:27

Associative Learning

1.7K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.7K

You might also read

Related Articles

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

Sort by
Same author

How foundation models will revolutionize robot swarms.

Science robotics·2026
Same author

Best-of-n decision making by human groups.

PloS one·2026
Same author

Collective decision making by embodied neural agents.

PNAS nexus·2025
Same author

Self-organizing nervous systems for robot swarms.

Science robotics·2024
Same author

Action-based confidence sharing and collective decision making.

iScience·2024
Same author

A blockchain-based information market to incentivise cooperation in swarms of self-interested robots.

Scientific reports·2023

Related Experiment Video

Updated: Mar 21, 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

13.6K

Incremental social learning in particle swarms.

Marco A Montes de Oca1, Thomas Stutzle, Ken Van den Enden

  • 1Institut de Recherches Interdisciplinaires et de Développements en Intelligence Artificielle (IRIDIA), Université Libre de Bruxelles (ULB), Brussels, Belgium. mmontes@ulb.ac.be

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 30, 2010
PubMed
Summary

Incremental social learning significantly enhances population-based optimization algorithms. New algorithms, Incremental Particle Swarm Optimizer (IPSO) and IPSOLS, show improved performance, especially on complex problems.

Related Experiment Videos

Last Updated: Mar 21, 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

13.6K

Area of Science:

  • Artificial Intelligence
  • Computational Intelligence
  • Optimization Algorithms

Background:

  • Scalability in multi-agent systems is a challenge.
  • Incremental Social Learning (ISL) offers a potential solution.
  • Population-based optimization algorithms can benefit from improved learning strategies.

Purpose of the Study:

  • To investigate the utility of ISL in enhancing population-based optimization.
  • To introduce and analyze two novel algorithms: IPSO and IPSOLS.
  • To evaluate the performance of ISL-enhanced PSO algorithms.

Main Methods:

  • Analytical derivation of the probability density function for the IPSO initialization rule.
  • Comparative performance analysis of IPSO and IPSOLS against other PSO variants and random restart local search.
  • Empirical evaluation on benchmark functions with varying fitness distance correlations.

Main Results:

  • IPSO and IPSOLS demonstrate superior performance compared to traditional PSO algorithms.
  • The proposed initialization rule in IPSO effectively biases new particles towards optimal solutions.
  • IPSOLS, incorporating local search, further boosts solution quality.

Conclusions:

  • Incremental social learning is a valuable technique for improving population-based optimization.
  • IPSO and IPSOLS represent effective advancements in Particle Swarm Optimization.
  • The benefits of ISL are pronounced in problems with challenging fitness landscapes.