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.

Index Terms—Swarm Intelligence and Perception, Social
Cognitive Maps, Social Foraging, Self-Organization, Distributed
Search and Optimization in Dynamic Environments.

I. INTRODUCTION
WARM 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

Vitorino Ramos, Carlos Fernandes and Agostinho Rosa, are with
LaSEEB-ISR Evolutionary Systems and BioMedical Eng. Lab., IST -
Technical University of Lisbon (IST), Av. Rovisco Pais, 1, TN 6.21, 1049-
001, Lisbon, PORTUGAL (corresponding author e-mails :
vramos@laseeb.org, cfernandes@laseeb.org, acrosa@laseeb.org). Second
author work was supported in part by FCT-PRAXIS XXI, Ministério da
Ciência, Tecnologia e Ensino Superior, under a PhD fellowship. Ajith
Abraham is with the School of Computer Science and Engineering, Chung-
Ang University, Seoul, South Korea. (e-mail: ajith.abraham@ieee.org ).
provision of a global model (Stan Franklin, Coordination
without Communication, talk at Memphis Univ., USA, 1996).
The well-know bio-inspired computational paradigms know as
ACO (Ant Colony Optimization algorithm [5]) based on trail
formation via pheromone deposition / evaporation, and PSO
(Particle Swarm Optimization [14]) are just two among many
successful examples. Yet, and in what specifically relates to
the biomimicry of these and other computational models, much
more can be of useful employ, namely the social foraging
behavior theories of many species, which can provide us with
consistent hints to algorithmic approaches for the construction
of social cognitive maps, self-organization [1,6], coherent
swarm perception and intelligent distributed search, with direct
applications in a high variety of social sciences and
engineering fields [25
30]. In the present work, we will
address the collective adaptation of a social community to a
cultural (environmental, contextual) or informational
dynamical landscape, represented here – for the purpose of
different experiments – by several 3D mathematical functions
that change over time. Our precise and final goal will be to
keep track of extrema on those environments. For instance,
typical applications of evolutionary optimization in static
environments involve the approximation of the extrema of
functions. On the contrary, for dynamic environments, the
interest is not to locate the extrema but to follow it as closely
as possible [12].
Flocks of migrating birds and schools of fish are familiar
examples of spatial self-organized patterns formed by living
organisms through social foraging. Such aggregation patterns
are observed not only in colonies of organisms as simple as
single-cell bacteria, as interesting as social insects like ants
and termites as well as in colonies of multi-cellular vertebrates
as complex as birds and fish but also in human societies [8].
Wasps, bees, ants and termites all make effective use of their
environment and resources by displaying collective “swarm”
intelligence. For example, termite colonies build nests with a
complexity far beyond the comprehension of the individual
termite, while ant colonies dynamically allocate labor to
various vital tasks such as foraging or defense without any
central decision-making ability [5]. Slime mould is another
perfect example. These are very simple cellular organisms with
limited motile and sensory capabilities, but in times of food
shortage they aggregate to form a mobile slug capable of
transporting the assembled individuals to a new feeding area.
Should food shortage persist, they then form into a fruiting
body that disperses their spores using the wind, thus ensuring
the survival of the colony [18].
Computational Chemotaxis in Ants and Bacteria
over Dynamic Environments
Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa and Ajith Abraham
S