by Marco A. Montes de Oca and Thomas Stützle March 2008
Submitted to the Evolutionary Computation and Multi-Agent Systems and Simulation Workshop (ECoMASS-2008).
This page contains all supplementary information that, for the sake of conciseness, was not included in the paper. Table of Contents |
Social learning is a mechanism that allows individuals to
acquire knowledge from others without incurring the costs of
acquiring it individually. Individuals that learn socially can
thus spend their time and energy exploiting their knowledge
or learning new things. In this paper, we adapt these ideas
for their application to both optimization and multiagent
learning. The approach consists of a growing population of
agents that learn socially as they become part of the main
population. We find that learning socially in an incremental
way can speed up the optimization and learning processes,
as well as improve the quality of the solutions and strategies
found.
Keywords: Social Learning, Particle Swarm Optimization, Multiagent
systems.
The following figures show the median solution quality improvement over time. Different population sizes are used for the traditional particle swarm optimization algorithm and different stagnation thresholds are used for the incremental social learning approach.
Ackley function - 100 dimensions | |||
---|---|---|---|
Best particle as model | Random particle as model | ||
Fully connected topology | Ring topology | Fully connected topology | Ring topology |
Rastrigin function - 100 dimensions | |||
---|---|---|---|
Best particle as model | Random particle as model | ||
Fully connected topology | Ring topology | Fully connected topology | Ring topology |
Rosenbrock function - 100 dimensions | |||
---|---|---|---|
Best particle as model | Random particle as model | ||
Fully connected topology | Ring topology | Fully connected topology | Ring topology |
Schwefel function - 100 dimensions | |||
---|---|---|---|
Best particle as model | Random particle as model | ||
Fully connected topology | Ring topology | Fully connected topology | Ring topology |
Step function - 100 dimensions | |||
---|---|---|---|
Best particle as model | Random particle as model | ||
Fully connected topology | Ring topology | Fully connected topology | Ring topology |
The following figures show the median collective reward over time. Different agent addition schedules are used for the implementation of the incremental social learning framework.