Swarm Intelligence

Generative Structures in Cities

by Kinda Al Sayed

Co-authored with Alasdair Turner and Sean Hanna

Research in the area of Space syntax tends to be centred on static representations of the built environment and its... more Research in the area of Space syntax tends to be centred on static representations of the built environment and its embedded social logic. Lacking for the most part the element of time, this synchronous representation cannot capture the evolutionary dynamics of urban systems. In this paper, we argue that the abstract values of space-time as a dual dimension play a key role as generators of city systems. Hence, we explore the driving forces that help reproduce growing spatial networks and yet preserve their structural properties. In two case studies; Manhattan and Barcelona, synchronic states of the growing systems are analysed. The states are separated by a certain radius of time. The analysis leads to regularities that may outline a generative model embedded in the pattern of growth and marked by alternating periods of expansion and pruning. In periods of expansion, a positive feedback process operates and takes the form of exponential addition of elements. The emergence of patches on the edges follows high values of choice and is subject to the temporal configurations of the grid. Once we observe the long-term time dimensionality, we note a change in the trend of the system as it reaches its maximum boundary. Following this change, another process of reinforcing feedback is introduced to the spatial network. This process involves intensifying sparse grid structures that have witnessed high gains in centrality in prior states and a process of pruning of poorly integrated elements. Both processes aim to differentiate the spatial structure of a city hence matching that of an organic grid. The findings yield that even at events of large scale planning interventions; cities adapt the local configurations of the new uniform parts to deform in such a way as to reproduce natural growth. In this manner, cities embody the intelligent collective minds of individuals. They are trade-off products of individuals’ decisions and they adapt their behaviour by prioritising a maximum parts-whole relationship that optimises access in the spatial network. We introduce these feedback processes under a framework of a plausible generative model to simulate city growth. The model is expected to both provide a better understanding of city growth and to aid design decision making on urban and regional scales.

An Eco-inspired Evolutionary Algorithm Applied to Numerical Optimization

by Rafael Stubs Parpinelli

Book: Theory and New Applications of Swarm Intelligence

by Rafael Stubs Parpinelli

Open Acces Book. Edited by Rafael Stubs Parpinelli and Heitor Silvério Lopes.

The field of research that studies the emergent collective intelligence of self-organized and decentralized simple... more The field of research that studies the emergent collective intelligence of self-organized and decentralized simple agents is referred to as Swarm Intelligence. It is based on social behavior that can be observed in nature, such as flocks of birds, fish schools and bee hives, where a number of individuals with limited capabilities are able to come to intelligent solutions for complex problems. The computer science community have already learned about the importance of emergent behaviors for complex problem solving. Hence, this book presents some recent advances on Swarm Intelligence, specially on new swarm-based optimization methods and hybrid algorithms for several applications. The content of this book allows the reader to know more both theoretical and technical aspects and applications of Swarm Intelligence.

On Swarm Optimality In Dynamic And Symmetric Environments

by Yaniv Altshuler

The field of multi agents and multi robotics has become increasingly popular during
the last two decades.more The field of multi agents and multi robotics has become increasingly popular during
the last two decades.

The motivation behind multi agents based systems is that many tasks can be
much efficiently completed by using multiple simple autonomous agents (robots,
software agents, etc.) instead of a single sophisticated one.

However, when examining such systems, one may be concerned of the price-tag
attached to the decentralized nature of swarm based approaches. Meaning, while
we simplify designs and control mechanisms in order to save costs and
computation resources, how far do our systems drift from optimality~?

This work examines this issue by constructing an optimal algorithm for the
\emph{Dynamic Cooperative Cleaners} problem. The performance of the \textbf{SWEEP} algorithm
is compared to this of an optimal algorithm. The results of this comparison
show that not only that the swarm algorithm produces close results to the
optimal solution, but also as the problem gets harder, the performance of the
two converge.

In addition, insightful results concerning optimal swarms in symmetric
environments are presented.

The Cooperative Hunters-Efficient Cooperative Search for Smart Targets Using Uav Swarms

by Yaniv Altshuler

Cooperative Cleaners: A Study In Ant Robotics

by Yaniv Altshuler

Was published in the International Journal of Robotics Research

In the world of living creatures, ``simple minded'' animals often cooperate to
achieve common goals with amazing... more In the world of living creatures, ``simple minded'' animals often cooperate to
achieve common goals with amazing performance. One can consider this idea in
the context of robotics, and suggest models for programming goal-oriented
behavior into the members of a group of simple robots lacking global
supervision. This can be done by controlling the local interactions between the
robot agents, to have them jointly carry out a given mission. As a test case we
analyze the problem of many simple robots cooperating to clean the dirty floor
of a non-convex region in $\mbox{\bf Z}^{2}$, using the dirt on the floor as
the main means of inter-robot communication.

Swarm Robotics for a Dynamic Cleaning Problem

by Yaniv Altshuler

Was published in IEEE Swarm Intelligence Symposium 2005

Several recent works considered multi agents robotics in static environments (e.g.
\cite{CC},... more Several recent works considered multi agents robotics in static environments (e.g.
\cite{CC}, \cite{GraphSearch1}, \cite{CoopRobot1} and others). In this work we
examine ways of operating in dynamic environments, in which changes may take place
regardless of the agents' activity. The work focuses on a dynamic variant of the
known \emph{Cooperative Cleaners} problem (described and analyzed in~\cite{CC}).
This problem assumes a grid, part of which is ``dirty'', when the ``dirty'' part is
a connected region of the grid. On this dirty region several agents move, each
having the ability to ``clean'' the place it is located in. The dynamic variant of
the problem involves a deterministic evolution of the environment, simulating a
spreading \emph{contamination}, or \emph{fire}. A cleaning protocol for the problem
is presented, as well as several analytic bounds for it. In addition, the work
contains simulative results for the proposed protocol.

Swarm Intelligence—Searchers, Cleaners and Hunters

by Yaniv Altshuler

Swarm ant robotics for a dynamic cleaning problem—upper bounds

by Yaniv Altshuler

Multi-agent Cooperative Cleaning of Expanding Domains

by Yaniv Altshuler

Published in the International Journal of Robotics Research, 2010

Several recent works considered multi-a(ge)nt robotics in static environments.
In this work we examine ways of... more Several recent works considered multi-a(ge)nt robotics in static environments.
In this work we examine ways of operating in dynamic environments, where
changes take place independently of the agents' activity. The work focuses on a
dynamic variant of the \emph{Cooperative Cleaners} problem, a problem that requires several simple agents to clean a connected region of ``dirty'' pixels in $\mbox{\bf Z}^{2}$. A number of simple agents move in this dirty region, each having the ability to ``clean''
the place it is located in. Their goal is to jointly clean the given dirty region. The dynamic variant of the problem involves a
deterministic expansion of dirt in the environment, simulating spreading of
\emph{contamination}, or \emph{fire}. Theoretical lower bounds for the problem are
presented, as well as various impossibility results.
A cleaning protocol for the problem is presented, and a wealth of experimental results
testing its performance in comparison to the lower bounds.
Several analytic upper bounds for the proposed protocol are also presented, accompanied with appropriate experimental results.

Efficient Cooperative Search of Smart Targets Using UAV Swarms

by Yaniv Altshuler

This work examines the \emph{Cooperative Hunters} problem, where a swarm of
unmanned air vehicles (\emph{UAVs})... more This work examines the \emph{Cooperative Hunters} problem, where a swarm of
unmanned air vehicles (\emph{UAVs}) is used for searching after one or more
``evading targets'', which are moving in a predefined area while trying to
avoid a detection by the swarm.

By arranging themselves into efficient geometric flight
configurations, the UAVs optimize their integrated sensing
capabilities, enabling the search of a maximal territory.

Multi-agent Physical A* with Large Pheromones

by Yaniv Altshuler

Swarm Intelligence – Searchers, Cleaners and Hunters

by Yaniv Altshuler

This work examines the concept of \emph{swarm intelligence} through three
examples of complex problems which are... more This work examines the concept of \emph{swarm intelligence} through three
examples of complex problems which are solved by a decentralized swarm of
simple agents. The protocols employed by these agents are presented, as well as
various analytic results for their performance and for the problems in general.
The problems examined are the problem of finding patterns within physical
graphs (e.g. \emph{k-cliques}), the \emph{dynamic cooperative cleaners}
problem, and a problem concerning a swarm of UAVs (unmanned air vehicles),
hunting an evading target (or targets). In addition, the work contains a
discussion regarding open questions and ongoing and future research in this
field.

The Complexity of Grid Coverage by Swarm Robotics

by Yaniv Altshuler

In this paper we discuss the task of efficiently using ant-like robotic agents for covering a connected region on the... more In this paper we discuss the task of efficiently using ant-like robotic agents for covering a connected region on the $\mbox{\bf Z}^{2}$ grid, whose shape and size are unknown in advance, and which expands at a given rate. This is done using myopic robots, with no ability to directly communicate with each other, where each robot is equipped with only $O(1)$ memory.

We show that regardless of the algorithm used, and the robots' hardware and software specifications, the minimal number of robots required in order to enable such coverage is $\Omega({\sqrt{n}})$ (where $n$ is the initial size of the connected region).

In addition, we show that when the region expands at a sufficiently slow rate, a team of $\Theta(\sqrt{n})$ robots could cover it in at most $O(n^{2} \ln n)$ time.

Regarding the coverage of non-expanding regions in the grid, we improve the current best known result of $O(n^{2})$ by demonstrating an algorithm of worse case completion time of $O(\frac{1}{k} n^{1.5} + n)$, and faster for shapes of perimeter length which is shorter than $O(n)$.

A Two-Step Binary Particle Swarm Optimization Approach for Routing in VLSI

by Amar Faiz Zainal Abidin

Zulkifli Md Yusof, Amar Faiz Zainal Abidin, Asrul Adam, Kamal Khalil, Jameel Abdulla Ahmed Mukred, Mohd Saberi Mohamad, M. Khalil Hani, Zuwairie Ibrahim. (2012) A Two-Step Binary Particle Swarm Optimization Approach for Routing in VLSI, 771-776. In ICIC Express Letters 6 (3).

Manipulation of wire sizing, buffer sizing, and buffer insertion are a few techniques that can be used to improve time... more Manipulation of wire sizing, buffer sizing, and buffer insertion are a few techniques that can be used to improve time delay in very large scale integration (VLSI) circuit routing. This paper enhances an existing approach, which is based on Particle Swarm Optimization (PSO) for solving routing problem in VLSI circuits. A two-step Binary Particle Swarm Optimization (BPSO) approach, which is based on BPSO, is chosen in this study to improve time delay through finding the best path of wire placement with buffer insertion from source to sink. The best path of wire placement is found in the first step by the first BPSO and then the second BPSO finds the best location of buffer insertion along the wire. A case study is taken to measure the performance of the proposed model and the result is obtained compared with the previous PSO approach for VLSI routing.

A Modified Computational Model of Ant Colony System in DNA Sequence Design

by Amar Faiz Zainal Abidin

Seri Mastura Mustaza, Amar Faiz Zainal Abidin, Zuwairie Ibrahim, Mohammad Amir Shamsudin, Abdul Rashid Husain, Jameel Abdulla Ahmed Mukred. (2011) A Modified Computational Model of Ant Colony System in DNA Sequence Design, 187-191. In 2011 IEEE Student Conference on Research and Development.

Many studies have focused in designing a set of good DNA sequences as it is one of the crucial tools in improving the... more Many studies have focused in designing a set of good DNA sequences as it is one of the crucial tools in improving the reliability and efficiency of DNA computing. In this paper, an improved model of Ant Colony System is developed in optimizing DNA sequences design. The proposed model suggests that each artificial ant represents a possible solution of the DNA sequences design problem. This differs from the previous Ant Colony System approached where a number of artificial ants are required to represent a possible solution. In the implementation, four objective measures and two constraint measures are employed to obtain a good set of DNA sequences. The performance of the proposed model is evaluated by comparing the result with existing Ant Colony System model and other published sequence design method. The experimental result shows that the proposed Ant Colony System model.

State Cyber Advocacy

by Alex Gekker

Dissipative particle swarm optimization

by Xiao-Feng Xie

DEPSO: Hybrid Particle Swarm With Differential Evolution Operator

by Xiao-Feng Xie

Adaptive particle swarm optimization on individual level

by Xiao-Feng Xie