Computational Chemotaxis in Ants and Bacteria over Dynamic Environments

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Computational Chemotaxis in Ants and Bacteria over Dynamic Environments

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by Vitorino Ramos , Carlos Fern , Agostinho C. Rosa , Ajith Abraham

BibTeX

@MISC{Ramos_computationalchemotaxis,
    author = {Vitorino Ramos and Carlos Fern and Agostinho C. Rosa and Ajith Abraham},
    title = {Computational Chemotaxis in Ants and Bacteria over Dynamic Environments},
    year = {}
}

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Abstract

Abstract — 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 DeJong test suite. Then, 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.

Keyphrases

dynamic environment computational chemotaxis cooperative foraging period ant colony final result certain chemical food molecule eusocial insect novel swarm intelligent algorithm similar stigmergic concept contradictory purpose similar natural mechanism real ant colony behavior collective adaptation recent model simple model abstract chemotaxis adaptive speed ssa collective intelligence multicellular organism global collective behaviour similar stigmergic feature severe dynamic optimization problem multimodal complex function present approach deal well-know dejong test suite unforeseen situation sophisticated pattern self-organized computational approach innate behavioural response social swarm directional stimulus artificial bacterial foraging

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