Paper Info
Title
Cooperative micro-particle swarm optimization
Abstract
Cooperative approaches have proved to be very useful in evolutionary computation due to their ability to solve efficiently high-dimensional complex problems through the cooperation of low-dimensional subpopulations. On the other hand, Micro-evolutionary approaches employ very small populations of just a few individuals to provide solutions rapidly. However, the small population size renders them prone to search stagnation. This paper introduces Cooperative Micro-Particle Swarm Optimization, which employs cooperative low-dimensional and low-cardinality subswarms to concurrently adapt different subcomponents of high-dimensional optimization problems. The algorithm is applied on high-dimensional instances of five widely used test problems with very promising results. Comparisons with the standard Particle Swarm Optimization algorithm are also reported and discussed.
Year
DOI
Venue
2009
10.1145/1543834.1543897
GEC Summit
Keywords
Field
DocType
cooperative approach,small population size,high-dimensional instance,low-dimensional subpopulations,high-dimensional optimization problem,cooperative micro-particle swarm optimization,cooperative low-dimensional,high-dimensional complex problem,standard particle swarm optimization,small population,swarm intelligence,optimization problem,particle swarm optimization,population size,cooperative
Particle swarm optimization,Derivative-free optimization,Mathematical optimization,Swarm behaviour,Computer science,Meta-optimization,Swarm intelligence,Multi-swarm optimization,Artificial intelligence,Imperialist competitive algorithm,Machine learning,Metaheuristic
Conference
Citations 
PageRank 
References 
5
0.42
14
Authors
1
Name
Order
Citations
PageRank
Konstantinos E. Parsopoulos119916.50