#ant colony optimization

[kats-lousy-learning-lab]

studynotes #9

sunday - 06.09.20 - 00:23

So i’m studying for my exam on swarm intelligence and boi. i wonder how long it will take for my brain to be un-ruined from thinking about ant colony optimization so much. Like everytime ants are mentioned - you know, those funky little insects? - my brain just goes “Oh - backward, forward or random ants?” and i would like for it to stop so i can enjoy nature documenteries again.

Anyways, this is the third time i am preparing for this exam (first time i failed, second time i got sick) and it’s just such a great illustration of how even the most interesting topic can not only go stale but get so fucking annoying frustrating that its not fun anymore. Like look. I love Optimization problems. And celluar automata (and maybe even simulation theory) but somebody please save me from it all. I am very afraid to fail this exam again (i mean i will be able to retake it with no problem thankfully cause my uni actually did listen to our concerns about the virus and all) but will i really have the strength to sit down and study it all over again? I am just praying to whoever is the patron saint of students at this point.

On a a more positive note tho, I did pass both my data base exams on the first try (this didn’t happen to me in so long it still feels unreal) so i got that going for me i guess.

My thesis is moving forward very slowly (and its not even my fault, natural language processing is just like that) and while that is a source of mild distress and heavy annoyance, its a problem for kat, who will have had her swarm intelligence exam. At least, with the thesis I know what my next steps will be, so its fine. I just wish things were a bit faster, so i could at least get done before christmas. and before more exams 0^0′

So Yeah, sorry this is just me venting my frustrations. If anything, I wish all of you the strength to press on through annoying projects and frustrating topics and whatever else you may struggle with.

TL;DR: just me whining during a brief summery of what i’ve been up to uni-wise

[projectchampionz]

Artificial Intelligence Network Load Balancing Using Ant Colony Optimization

Artificial Intelligence Network Load Balancing Using Ant Colony Optimization

ARTIFICIAL INTELLIGENCE NETWORK LOAD BALANCING USING ANT COLONY OPTIMIZATION

Introduction

Ants first evolved around 120 million years ago, took form in over 11,400 different species, and are considered one of the most successful insects due to their highly organised colonies, sometimes consisting of millions of ants.

One particular notability of ants is their ability to create “ant streets”.…

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[projectchampionz]

MULTI- PARAMETER OPTIMIZATION OF COST ENTROPY FOR REINFORCED CONCRETE OFFICE BUILDING PROJECTS USING ANT COLONY OPTIMIZATION

MULTI- PARAMETER OPTIMIZATION OF COST ENTROPY FOR REINFORCED CONCRETE OFFICE BUILDING PROJECTS USING ANT COLONY OPTIMIZATION

RESEARCH PROJECT TOPIC ON MULTI- PARAMETER OPTIMIZATION OF COST ENTROPY FOR REINFORCED CONCRETE OFFICE BUILDING PROJECTS USING ANT COLONY OPTIMIZATION

TABLE OF CONTENT

Title Page———i

Certification——–ii

Dedication———iii

Acknowledgement——-iv

Abstract ———vi

Table of Content——–vii

Chapter One

1.0 Introduction ——-1

1.1 Statement of Problem——4

1.2 Purpose of the Study——5

1.3 Significance of Study——8

1.…

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[transhumanitynet]

AI Abstract Series E14 - Multi-agent distributed framework for swarm intelligence

AI Abstract Series E14 – Multi-agent distributed framework for swarm intelligence

Welcome to the Technocracy A.I. Abstract Series for Published Scientific Work in the A.I. and Artificial General Intelligence field.

Today’s paper is titled: Multi-agent distributed framework for swarm intelligence

https://soundcloud.com/technocracypodcast/ai-abstract-series-e14-multi-agent-distributed-framework-for-swarm-intelligence

Authored By Sorin Ilie and Costin Badica

Abstract: This paper…

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[transhumanitynet]

AI Abstract Series E14 - Multi-agent distributed framework for swarm intelligence

Welcome to the Technocracy A.I. Abstract Series for Published Scientific Work in the A.I. and Artificial General Intelligence field.

Today’s paper is titled: Multi-agent distributed framework for swarm intelligence

Authored By Sorin Ilie and Costin Badica

Abstract: This paper presents a multi-agent distributed framework for Swarm Intelligence (SI) based on our previous work ACODA(Ant Colony Optimization on a Distributed Architecture). Our framework can be used to distribute SI algorithms for solving graph search problems on a computer network. Examples and experimental results are given for SI algorithms of: Ant Colony System (ACS) and Bee Colony Optimization (BCO). In order to use the framework, the SI algorithms must be conceptualized to take advantage of the inherent parallelism determined by their analogy with natural phenomena (biological, chemical, physical, etc.): (i) the physical environment of the swarm entities is represented as a distributed multi-agent system and (ii)entities’ movement in the physical environment is represented as messages exchanged asynchronously between the agents of the problem environment. We present initial experimental results that show that our framework is scalable. We then compare the results of the distributed implementations of BCO and ACS algorithms using our framework. The conclusion was that our approach scales better when implementing the ACS algorithm but is faster when implementing BCO.

As always thank you for listening to the Technocracy Abstract Series and a special thank you for our sponsors the Foundation, Transhumanity.net and the AGI Laboratory.

https://www.academia.edu/37463769/Multi-agent_distributed_framework_for_swarm_intelligence

and

AI Abstract Series E14 – Multi-agent distributed framework for swarm intelligence

    AI Abstract Series E14 – Multi-agent distributed framework for swarm intelligence was originally published on transhumanity.net

[islam-elmasry]

Ant Colony Optimization

Ant Colony Optimization (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.

Also,  ACO is a meta-heuristic approach. A meta-heuristic is a higher-level procedure or heuristic designed to find, generate, or select heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.

HISTORY

In 1992, Marco Dorigo proposed the first version of ACO, which searches for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food.

STEPS

1. A trail of ants goes from their colony in random directions searching for a food source.

2. While walking, ant leaves "Pheromone" along with its path.

3. When finding a food, an ant comes back to the colony, but it walks in the path with higher pheromone level.

4. The probabilities of other paths being chosen will converge to the strangest reinforced path.

 FORMAL REPRESENTATION

A model P = (S, Ω, f)

S is a search space defined over a finite set of discrete decision variables;

Ω is a set of constraints among the variables; and 

f: S→R+0 is an objective function to be minimized (as maximizing over f is the same as minimizing over −f, every COP can be described as a minimization problem). 

Algorithm (pseudo code):

Set parameters, initialize pheromone trails SCHEDULE_ACTIVITIES   ConstructAntSolutions   DaemonActions    {optional}   UpdatePheromones END_SCHEDULE_ACTIVITIES

Example:

 One of the most important problems that can be solved using ACO is the Traveling Salesperson Problem (TSP).

Problem Statement:

Given a set of cities and the distances between each pair of cities, what is the shortest possible tour that visits each city exactly once, and returns to the starting city?

Here is an excellent Jupyter Notebook contains the implementations of different techniques by Peter Norvig

Applications:

NP-hard combinatorial optimization problems

Classification

Games

Routing in telecommunication networks