7 research outputs found
Computational Chemotaxis in Ants and Bacteria over Dynamic Environments
Chemotaxis can be defined as an innate behavioural response by an organism to
a directional stimulus, in which bacteria, and other single-cell or
multicellular organisms direct their movements according to certain chemicals
in their environment. This is important for bacteria to find food (e.g.,
glucose) by swimming towards the highest concentration of food molecules, or to
flee from poisons. Based on self-organized computational approaches and similar
stigmergic concepts we derive a novel swarm intelligent algorithm. What strikes
from these observations is that both eusocial insects as ant colonies and
bacteria have similar natural mechanisms based on stigmergy in order to emerge
coherent and sophisticated patterns of global collective behaviour. Keeping in
mind the above characteristics we will present a simple model to tackle the
collective adaptation of a social swarm based on real ant colony behaviors (SSA
algorithm) for tracking extrema in dynamic environments and highly multimodal
complex functions described in the well-know De Jong test suite. Later, for the
purpose of comparison, a recent model of artificial bacterial foraging (BFOA
algorithm) based on similar stigmergic features is described and analyzed.
Final results indicate that the SSA collective intelligence is able to cope and
quickly adapt to unforeseen situations even when over the same cooperative
foraging period, the community is requested to deal with two different and
contradictory purposes, while outperforming BFOA in adaptive speed. Results
indicate that the present approach deals well in severe Dynamic Optimization
problems.Comment: 8 pages, 6 figures, in CEC 07 - IEEE Congress on Evolutionary
Computation, ISBN 1-4244-1340-0, pp. 1009-1017, Sep. 200
Varying the Population Size of Artificial Foraging Swarms on Time Varying Landscapes
Abstract. Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) entities interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with which it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. In this paper we present a Swarm Search Algorithm with varying population of agents. The swarm is based on a previous model with fixed population which proved its effectiveness on several computation problems. We will show that the variation of the population size provides the swarm with mechanisms that improves its self-adaptability and causes the emergence of a more robust self-organized behavior, resulting in a higher efficiency on searching peaks and valleys over dynamic search landscapes represented here β for the purpose of different experiments β by several three-dimensional mathematical functions that suddenly change over time. We will also show that the present swarm, for each function, self-adapts towards an optimal population size, thus self-regulating.