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Biologycal Form Finding with Swarm Behav

Bio Form I Finding

Biologycal Form Finding with Swarm Behavior
Biologycal Form Finding with Swarm Behavior


Academic Study




  • Rhyno + Grasshopper

Bio Form I Finding


This project aims to seek a new form finding system using swarm intelligence on stress analysed topologycal geometrics. In the beginning, the survey was inspired by termite behavior modelling. Then it improved to general topologycal form finding system. The main approach of the project is ‘’Can we bio-computationally manipulate a form to suit our purpose?’’. Bio-computational research in geometric formations lead to bones as a model for developing a biologically responsive material. 

Concerning this issue, we will use Grasshopper’s Millipede Tools in our project for simulating the experiments in digital media. This add-on includes the implementations of stress analysis, decomposition and optimization of structures. After then, it outputs the new optimal form of structure geometry which is what we actually need. 


Swarm Behavior


As a term, swarming is applied particularly to insects, but can also be applied to any other  animal that exhibits swarm behaviour. The term flocking is usually used to refer specifically  to swarm behaviour in birds, herding to refer to swarm behaviour in quadrupeds, shoaling  or schooling to refer to swarm behaviour in fish.

Mathematical Models


1. Separation: Move in the same direction as your neighbors

2. Alignment: Remain close to your neighbors

3. Cohesion: Avoid collisions with your neighbors

Early studies of swarm behavior employed mathematical models to simulate and understand the behavior. The simplest mathematical models of animal swarms generally represent individual animals as following three rules: 

Swarm Intelligence


Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of “intelligent” global behavior, unknown to the individual agents. Examples in natural systems of SI include ant colonies, bird flocking, animal herding, bacterial growth, fish schooling and microbial intelligence. The application of swarm principles to robots is called swarm robotics, while ‘swarm intelligence’ refers to the more general set of algorithms. ‘Swarm prediction’ has been used in the context of forecasting problems.   


Adaptive Flock

There are two kinds of geometries will used in this project.

Each agent would have now a variable speed, with a common minimum and maximum for all agents. In case of collision trajectory, the agent will slow down. In the absence of collision, the agent will steadily speed up until it reaches its maximum. This means that in the event of a ‘conflict’ space, or an area where one agent detects many collisions consecutively, agents will cluster; since their speed is low, they will have the inertia to remain there, where as faster ‘free’ agents in the neighbourhood will be easily attracted to the area. The information about collision areas is therefore stored in the speed of the agents. Speeding up will be the equivalent of forgetting in the system.


Shell Geometry

Line Geometry




The methodology of the research is exploring the emergent behaviour of interaction between members of swarm might be used to form finding. The system is established with Rhinoceros Grasshopper and many kind of tools. Grasshopper’s Millipede Tools in our project for simulating the experiments in digital media. This add-on includes the implementations of stress analysis, decomposition and optimization of structures. After then, it outputs the new optimal form of structure geometry which is what we are looking for. The possibilities of this approach as an optimisation mechanism is the easy understanding of the relation between the search mechanism and the solution space. The optimal solutions and the way of the research are the main purpose of this survey. 

Test Field


The experiments of the project are based on simulating the physical rules of the real world. In order to achieve this, a hypothetical ground surface was created using gravity. The base created for this study was called "base". The base is a geometric form in the model described in our system. In this case, it is defined as a rectangular prism.


In order to make some structural simulations, the study was carried out by adding loads on this ground. Starting from the effect of the loads on the base, the tension of the loads on the base was monitored. A stress layer map was created and analyzed.


The stress layer map was created to guide the herd movement. The herd movement principle was designed to move by following the regional colors of this stress layer map. The behavior of the swarm is based on vector forces. The colors (red, blue and green) have different force values ​​that dominate the movements of the members of the swarm. According to the study principle, the region where red (the most stressed region) wants to move away from the swarm individuals, the blue (most tension-free region) is the place where the swarm individuals want to reach.

The Stress Layer Map



This test is about to learn to how will be under the influence of the 5 loads which are placed bilaterally. The test has been taken 1 minute.

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