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CEC
2007
IEEE
15 years 6 months ago
Parallel learning in heterogeneous multi-robot swarms
Abstract— Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using unsupervised learning techniques w...
Jim Pugh, Alcherio Martinoli
BMCBI
2006
146views more  BMCBI 2006»
14 years 11 months ago
Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influen...
Michael Meissner, Michael Schmuker, Gisbert Schnei...
HIS
2009
14 years 9 months ago
A Particle Swarm Optimization with Feasibility-Based Rules for Mixed-Variable Optimization Problems
A Particle Swarm Optimization algorithm with feasibility-based rules (FRPSO) is proposed in this paper to solve mixed-variable optimization problems. An approach to handle various ...
Chao-Li Sun, Jian-Chao Zeng, Jeng-Shyang Pan
GECCO
2008
Springer
155views Optimization» more  GECCO 2008»
15 years 22 days ago
Integrating user preferences with particle swarms for multi-objective optimization
This paper proposes a method to use reference points as preferences to guide a particle swarm algorithm to search towards preferred regions of the Pareto front. A decision maker c...
Upali K. Wickramasinghe, Xiaodong Li
TSMC
2010
14 years 6 months ago
Particle Swarm Optimization With Composite Particles in Dynamic Environments
In recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with c...
Lili Liu, Shengxiang Yang, Dingwei Wang