Titlebook: Swarm Intelligence; 7th International Co Marco Dorigo,Mauro Birattari,Thomas Stützle Conference proceedings 2010 Springer-Verlag Berlin Hei

使服水土

MOT

Heterogeneous Particle Swarm Optimizations proposed in this paper, where particles are allowed to follow different search behaviours selected from a behaviour pool, thereby efficiently addressing the exploration–exploitation trade-off problem. A preliminary empirical analysis is provided to show that much can be gained by using heterogeneous swarms.

CRACY

Modern Continuous Optimization Algorithms for Tuning Real and Integer Algorithm Parametersinuous optimization algorithms instead of using a tedious, and error-prone, hands-on approach. In this paper, we study the performance of several continuous optimization algorithms for the algorithm parameter tuning task. As case studies, we use a number of optimization algorithms from the swarm intelligence literature.

察覺

運(yùn)動的我

Uncultured

A Robotic Validation of the Attractive Field Model: An Inter-disciplinary Model of Self-regulatory S that constitute a first validation of attractive filed model as a mechanism for MRTA and as a multi-disciplinary model of self-organisation in social systems. Our experiments used 16 e-puck robots in a 2m x 2m area.

forebear

A Thermodynamic Approach to the Analysis of Multi-robot Cooperative Localization under Independent Exing assumptions are used during the analysis. The goal is not to derive hard lower or upper bounds but rather to characterize the robots expected behavior. In particular, to predict the expected localization error. The predictions were validated using simulations. We believe that this kind of analysis can be beneficial in many other cases.

BRIEF

An Efficient Optimization Method for Revealing Local Optima of Projection Pursuit Indicesmature convergence to local optima. This method called Tribes is a hybrid Particle Swarm Optimization method (PSO) based on a stochastic optimization technique developed in [2]. The computation is fast even for big volumes of data so that the use of the method in the field of projection pursuit fulfills the statistician expectations.

打火石

Ant Colony Optimisation for Ligand Dockingroblem resulting in a high-performing optimisation approach. We discuss certain aspects of the hybridisation strategy including the integration of . into the search process and compare the performance to the . currently used in GOLD.

保守

Automatic Configuration of Multi-Objective ACO Algorithmshms clearly outperform the best performing MOACO algorithms that have been proposed in the literature. As far as we are aware, this paper is also the first to apply automatic algorithm configuration techniques to multi-objective stochastic local search algorithms.