Coalitional Search and Swarm Dynamics

2015 ◽  
pp. 171-244
Keyword(s):  
Swarm Dynamics

scholarly journals Brownian Swarm Dynamics and Burgers’ Equation with Higher Order Dispersion

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 57
Author(s):  
Max-Olivier Hongler
Keyword(s):  
Burgers Equation ◽  
Higher Order ◽  
Underlying Structure ◽  
Swarm Dynamics ◽  
Special Cases ◽  
Kink Waves ◽  
Systematic Derivation ◽  
Fifth Order ◽  
Higher Order Dispersion ◽  
Order Dispersion

The concept of ranked order probability distribution unveils natural probabilistic interpretations for the kink waves (and hence the solitons) solving higher order dispersive Burgers’ type PDEs. Thanks to this underlying structure, it is possible to propose a systematic derivation of exact solutions for PDEs with a quadratic nonlinearity of the Burgers’ type but with arbitrary dispersive orders. As illustrations, we revisit the dissipative Kotrweg de Vries, Kuramoto-Sivashinski, and Kawahara equations (involving third, fourth, and fifth order dispersion dynamics), which in this context appear to be nothing but the simplest special cases of this infinitely rich class of nonlinear evolutions.

Formulas

2019 ◽  
Author(s):  
Sebastian Vehlken
Keyword(s):  
Mathematical Models ◽  
Information Transmission ◽  
Empirical Data ◽  
Dynamic Processes ◽  
Geometric Form ◽  
Local Behavior ◽  
Fish Schools ◽  
Swarm Dynamics ◽  
Technical Vocabulary

Under the term formulas, this chapter investigates complementary strategies in order to describe the dynamics and functions of biological collectives. It examines how, on the basis of patchy empirical data, attempts were made to construct mathematical models concerned with the geometric form of fish schools or with the algorithms of the local behavior of swarm individuals. It thereby follows traces which link biological swarm research to cybernetic ideas of ‘communication’ or ‘information transmission.’ Equipped with a new technical vocabulary, researchers began to describe swarms as ‘systems’ and were able to conceive of them in new ways. Nevertheless, the first approaches to simulating swarm dynamics in the 1970s received little attention, a fact that was likely due to the inability at the time to display dynamic processes visually.

scholarly journals IMITATION, PROXIMITY, AND GROWTH — A COLLECTIVE SWARM DYNAMICS APPROACH

2019 ◽  
Vol 22 (05) ◽  
pp. 1950011
Author(s):  
OLIVIER GALLAY ◽  
FARIBA HASHEMI ◽  
MAX-OLIVIER HONGLER
Keyword(s):  
Economic Growth ◽  
Traveling Wave ◽  
Stochastic Environment ◽  
Balanced Growth ◽  
Diffusive Growth ◽  
Swarm Dynamics ◽  
Novel Approach ◽  
Dependent Processes

This paper is based on the premise that economic growth is driven by an interplay between innovation and imitation in an economy composed of interacting firms operating in a stochastic environment. A novel approach to modeling imitation is presented based on range-dependent processes that describe how firms consider proximity when imitating peers who are found in a given neighborhood in terms of productivity. Using a particularly tractable approach, we are able to analyze how drastically different economic growth scenarios emerge from different imitation strategies. These emerging scenarios range from diffusive growth where the variance of productivity grows indefinitely, to balanced growth described by a traveling wave with fixed variance. The latter scenario is sustained only when imitation strength among firms exceeds a critical bifurcation threshold.

Reinforcement Learning Adversarial Swarm Dynamics

2020 ◽  
Author(s):  
Davis S. Catherman ◽  
Cory Neville ◽  
Joshua Bloom ◽  
Samuel S. White
Keyword(s):  
Reinforcement Learning ◽  
Swarm Dynamics

Interplay between signaling network design and swarm dynamics

2016 ◽  
Vol 4 (2) ◽  
pp. 244-265 ◽  
Author(s):  
ANDRÉ SEKUNDA ◽  
MOHAMMAD KOMAREJI ◽  
ROLAND BOUFFANAIS
Keyword(s):  
Network Design ◽  
Information Transfer ◽  
Network Models ◽  
Signaling Network ◽  
Spectral Graph Theory ◽  
Theoretic Approach ◽  
Collective Behaviors ◽  
Global Consensus ◽  
Swarm Dynamics ◽  
The Impact

AbstractDistributed information transfer is of paramount importance to the effectiveness of dynamic collective behaviors, especially when a swarm is confronted with complex environmental circumstances. Recently, the signaling network of interaction underlying such effective information transfers has been revealed in the particular case of bird flocks governed by a topological interaction. Such biological systems are known to be evolutionary optimized, but are also constrained by the very nature of the signaling mechanisms—owing to intrinsic limitations in sensory modalities—enabling communication among individuals. Here, we propose that artificial swarm design can be tackled from the angle of signaling network design. To this aim, we use different network models to investigate the impact of some network structural properties on the effectiveness of a specific emergent swarming behavior, namely global consensus. Two new network models are introduced, which together with the well-known Watts–Strogatz model form the basis for an analysis of the relationship between clustering, shortest path and speed to consensus. A network-theoretic approach combined with spectral graph theory tools are used to propose some signaling network design principles. Eventually, one key design principle—a concomitant reduction in clustering and connecting path—is successfully tested on simulations of swarms of self-propelled particles.

Study On Swarm Dynamics Converted Into Complex Network

2016 ◽  
Author(s):  
Michal Pluhacek ◽  
Roman Senkerik ◽  
Jakub Janostik ◽  
Adam Viktorin ◽  
Ivan Zelinka
Keyword(s):  
Complex Network ◽  
Swarm Dynamics

ORIENTATION-INDUCED PATTERN FORMATION: SWARM DYNAMICS IN A LATTICE-GAS AUTOMATON MODEL

1996 ◽  
Vol 06 (09) ◽  
pp. 1735-1752 ◽  
Author(s):  
ANDREAS DEUTSCH
Keyword(s):  
Pattern Formation ◽  
Lattice Gas ◽  
Collective Motion ◽  
Particle Density ◽  
Navier Stokes ◽  
Navier Stokes Equation ◽  
Interaction Operator ◽  
Swarm Dynamics ◽  
Lattice Gas Models ◽  
Non Local

Swarming patterns might arise not just at organismic levels (bird and fishes exhibiting particularly striking examples) but even at cellular and intracellular scales whenever “collective motion” of biological or chemical entities is involved. Examples are the swarming of myxobacteria and ants, aggregation and slug pattern formation of the slime mold Dictyostelium discoideum, or intracellular network dynamics of actin filaments. Here a stochastic process — discrete in space and time — is developed, the “swarm lattice-gas automaton”. For some lattice-gas models (in physics and chemistry) it was demonstrated that the limit behavior resembles known master equations by means of expectation values of suitably chosen microscopic variables. In particular, for the Navier–Stokes equation the derivation of a continuous macroscopic description from discrete microdynamic equations was shown. The “swarm lattice-gas automaton” possesses a non-local integral-like interaction operator. Particles (cells, organisms) are assigned some orientation (and fixed absolute velocity) which might change by means of interaction with other members of the swarm within a given “region of perception”. The corresponding microdynamical equation is given and results of numerical experiments are shown. Simulations exhibit a variety of aggregation patterns which are distinguished by means of microscopic and macroscopic variables. The influence of a sensitivity parameter and particle density on pattern formation is examined systematically.

Sign in / Sign up

Export Citation Format

Share Document