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CEC
2007
IEEE
15 years 7 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»
15 years 1 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 11 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 2 months 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 7 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