This paper describes and investigates a swarm intelligence system with similarity-oriented behavioral rules, hierarchical clustering and evolution by random mutation. The evolutionary scheme is based on the Bak-Sneppen model of co-evolution between interacting species. The swarm of species, in this case, is randomly distributed on a 2-dimensional grid of nodes. The number of nodes is larger than the swarm size and the species are allowed to move on the grid. The rule that defines the movement of the species through the gird is based on the similarity between the species’ fitness values and the ranking of those same values within the entire population. Meanwhile, the fitness values are modified using the rules of a 2-dimensional Bak-Sneppen model. The system is intended to be a framework for metaheuristics with spatially structured populations and we show that it displays the desired characteristics for that purpose. Furthermore, these characteristics emerge as global patterns from the local interaction of the species. Without requiring the tuning of control parameters to precise values, the system seems to self-organize into a critical state between randomness and order.

1. Adapting the Bak-Sneppen Model to a
Dynamic and Partially Connected Grid of
Hierarchical Species
C.M. Fernandes (Technical Univ. of Lisbon), J.L.J. Laredo, J.J. Merelo, C. Cotta, A.C.Rosa
This paper investigates the behaviour of a variant of
the Bak-Sneppen model in which the species move on
2D habitat according to a simple set of rules.
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time-steps, t
G(s) average min
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time-steps, t
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Proposed
2D Bak-
Sneppen
Model
Standard 2D Bak-Sneppen model Standard 2D Bak-Sneppen model
Standard 1D Bak-Sneppen model
Spectral density of the distance between neighbours Gap functions and average and
minimum fitness
Periods of statis vs frequency (log-log)
Model without mutations