456 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
Mathematical models for chemotaxis and their applications in self-organisation phenomena
Chemotaxis is a fundamental guidance mechanism of cells and organisms,
responsible for attracting microbes to food, embryonic cells into developing
tissues, immune cells to infection sites, animals towards potential mates, and
mathematicians into biology. The Patlak-Keller-Segel (PKS) system forms part of
the bedrock of mathematical biology, a go-to-choice for modellers and analysts
alike. For the former it is simple yet recapitulates numerous phenomena; the
latter are attracted to these rich dynamics. Here I review the adoption of PKS
systems when explaining self-organisation processes. I consider their
foundation, returning to the initial efforts of Patlak and Keller and Segel,
and briefly describe their patterning properties. Applications of PKS systems
are considered in their diverse areas, including microbiology, development,
immunology, cancer, ecology and crime. In each case a historical perspective is
provided on the evidence for chemotactic behaviour, followed by a review of
modelling efforts; a compendium of the models is included as an Appendix.
Finally, a half-serious/half-tongue-in-cheek model is developed to explain how
cliques form in academia. Assumptions in which scholars alter their research
line according to available problems leads to clustering of academics and the
formation of "hot" research topics.Comment: 35 pages, 8 figures, Submitted to Journal of Theoretical Biolog
Description and composition of bio-inspired design patterns: a complete overview
In the last decade, bio-inspired self-organising mechanisms have been applied to different domains, achieving results beyond traditional approaches. However, researchers usually use these mechanisms in an ad-hoc manner. In this way, their interpretation, definition, boundary (i.e. when one mechanism stops, and when another starts), and implementation typically vary in the existing literature, thus preventing these mechanisms from being applied clearly and systematically to solve recurrent problems. To ease engineering of artificial bio-inspired systems, this paper describes a catalogue of bio-inspired mechanisms in terms of modular and reusable design patterns organised into different layers. This catalogue uniformly frames and classifies a variety of different patterns. Additionally, this paper places the design patterns inside existing self-organising methodologies and hints for selecting and using a design patter
A Hybrid Bacterial Swarming Methodology for Job Shop Scheduling Environment
Optimized utilization of resources is the need of the hour in any manufacturing system. A properly planned schedule is often required to facilitate optimization. This makes scheduling a significant phase in any manufacturing scenario. The Job Shop Scheduling Problem is an operation sequencing problem on multiple machines with some operation and machine precedence constraints, aimed to find the best sequence of operations on each machine in order to optimize a set of objectives. Bacterial Foraging algorithm is a relatively new biologically inspired optimization technique proposed based on the foraging behaviour of E.coli bacteria. Harmony Search is a phenomenon mimicking algorithm devised by the improvisation process of musicians. In this research paper, Harmony Search is hybridized with bacterial foraging to improve its scheduling strategies. A proposed Harmony Bacterial Swarming Algorithm is developed and tested with benchmark Job Shop instances. Computational results have clearly shown the competence of our method in obtaining the best schedule
Algorithms for Olfactory Search across Species
Localizing the sources of stimuli is essential. Most organisms cannot eat, mate, or escape without knowing where the relevant stimuli originate. For many, if not most, animals, olfaction plays an essential role in search. While microorganismal chemotaxis is relatively well understood, in larger animals the algorithms and mechanisms of olfactory search remain mysterious. In this symposium, we will present recent advances in our understanding of olfactory search in flies and rodents. Despite their different sizes and behaviors, both species must solve similar problems, including meeting the challenges of turbulent airflow, sampling the environment to optimize olfactory information, and incorporating odor information into broader navigational systems
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A survey of swarm intelligence for dynamic optimization: algorithms and applications
Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given
The Emergence of Lines of Hierarchy in Collective Motion of Biological Systems
The emergence of large scale structures in biological systems, and in
particular the formation of lines of hierarchy, is observed in many scales,
from collections of cells to groups of insects to herds of animals. Motivated
by phenomena in chemotaxis and phototaxis, we present a new class of alignment
models which exhibit alignment into lines. The spontaneous formation of such
``fingers" can be interpreted as the emergence of leaders and followers in a
system of identically interacting agents. Various numerical examples are
provided, which demonstrate emergent behaviors similar to the ``fingering''
phenomenon observed in some phototaxis and chemotaxis experiments; this
phenomenon is generally known as a challenging pattern to capture for existing
models. The novel pairwise interactions provides a fundamental mechanism by
which agents may form social hierarchy across a wide range of biological
systems
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