#optmization

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

As a new player, this solution looked very neat to me. Everything you make in Opus is tremendously satisfying to watch, so even if you’re only muddling through the game you get to feel smart. But there’s also enormous scope for improvement, in three different directions.

It could be cheaper. [...] This solution involves 70g worth of gear. The cheapest one I’ve designed since only costs 40g.

It could be smaller. Not by much, this is the metric I scored best on. [...]

And my solution could be faster. It could be a lot faster. My first solution took 63 cycles, then my friend Alex beat me with 53, so I made that one which only takes 45. I was pretty pleased with myself. Until the leaderboards told me my friend Jeep had done it in 24. Twenty-four?! You can do this almost twice as fast as my fast solution? Opus Magnum lets you see your friends scores but doesn’t show you their solutions, and I’m actually glad. I wanted to figure this out for myself.

Basic Search Engine Optmization

by B.Garry

Basic Search Engine Optmization

Search engine optimization is not as difficult the way many people make it out to be. It seems mysterious, but let me reveal how this simple thing can be done without a line of code, without breaking any law, and still generate a ton of extra traffic to your site.

Basically, search engine optimization is about getting higher rankings. But that’s beside…

Nancy ‘if i goof up today, i will need to optimize self for better management’

Freida ‘Means?’

Nancy “Am bad at time management. Spend 10 minutes talking away with friends when I have breakfast leading to delay in reaching office”

Freida “Me too around 10 minutes keep interacting during tea time and lunch”

Enters Rob who is the founder of this startup where Nancy and Freida overhears the…