Download Particle Swarm Optimization mini tutorial

PSO and ASO
Variants/Hybrids/Example Applications
& Results
Lecture 12 of Biologically Inspired Computing
Purpose:
Not just to show variants/etc … for these specific algorithms,
but to indicate these as examples of typical ways in which
these and similar methods (especially EAs) are engineered,
combined, etc … in attempt to get the best performance possible
on a given problem.
Hybridisation
•A very common algorithm engineering method.
•It simply means: creating an algorithm that combines aspects of two
or more different algorithms
• Very many hybridisations have been researched.
• E.g.:
• ACO + Local Search (e.g. hillclimbing)
1. Run 1 generation of ACO with m ants
2. Run LS on each of the m ants
3. Until a termination condition is
reached, return to 1.
• EA + Local Search
• The same story, but these are called Memetic Algorithms
Hybridisation II
•
Hybridisation with appropriate constructive heuristics is
common, as you know. E.g.
1. Generate Initial Population with several runs of a
stochastic constructive heuristic.
2. Carry on from there using ACO, PSO, or whatever.
•
Another very common hybrid approach:
1. Run your standard, single method (e.g. an EA).
2. Take the best solution from 1, and run Local Search
(e.g. hillclimbing) on it for a while to try to
find improvements.
Adaptive Parameters
All the algorithms we have looked at have parameters. E.g. Mutation
rate, crossover rate, population size, number of ants, C1 and C2 in PSO,
and so on.
Instead of (say) keeping the mutation strength at 1 gene per chromosome,
why not vary it during the algorithm run? Why?
ALL search-based optimisation algorithms balance two things:
Exploration: to boldly go where no chromosome/particle/ant/…
has gone before. Reaching out to new areas of the search space.
Exploitation: focussing narrowly on investigating the region close
to the best so far point, in search of better points close by.
Too much exploration? Too often stuck in suboptimal areas
Too much exploitation? Much too slow progress – high risk/low reward
Questions re Adaptive Operators
In PSO, let d be the average distance that a particle moves, at the
beginning of the run.
•How does d change with time?
•What is d like after running the algorithm for a very long time?
Is this a good thing?
•How important is C2 (influence of global best) at the beginning
of the run, and at the end of the run?
•In an EA with both crossover and mutation? How important is
mutation at the end and at the beginning of the run.
Various ways to control adaptation
In EAs, it may seem sensible for mutation strength to start low, say
at mS and gradually increase linearly, say to mF. (e.g. perhaps 1/L
and 5/L). Given a limit of 1,000 generations, say, the rate will
increase by a simply pre-calculated amount per generation.
But this is not really adapting to the current situation in the search
process. It may be fine for some problems, but on others the
population may converge at 100 generations, or it may still be very
diverse at 1,000 generations, or (of course), the rates themselves
may be very unsuitable for the problem.
So it is common to use `truly’ adaptive techniques that react to the
current state of the population. The mutation rate may be based
directly on measures of diversity in the current population, measures
of success in the last few mutation events, and so on.
Useful metrics
Distance between two individuals:
Hamming distance: no. of genes that are different
Euclidean distance: only suitable for …. ?
Edge distance:
Permutations 1. ABCDEF and 2. EDFABC
are how different?
Edge-sets: 1. AB, BC, CD, DE, EF, FA
2. AB, BC, CE, ED, DF, FA
There are various ways to base a distance metric on edge sets
Distance metric for grouping problems?
Population diversity:
Mean values and variances for each gene.
Mean distance between pairs of individuals
Measure-based Adaptation
Mutation strength = mmin  (1  Dcurrent)(mmax  mmin)
(where D varies from 0 (all the same) to 1 (maximally different)
Or: a form of genewise mutation:
Prob of mutating gene rises as the variance of that gene in the
population falls.
Or: Run EA as normal, but:
pop diversity > Dthresh: only use crossover
pop diversity < Dthresh – only use mutation.
Or: Run PSO as normal, but:
pop diversity < Dthresh: do one iteration where the particle
velocities are randomly chosen, then move on.
The Vehicle Routing Problem
Depot
Customer
A number of vehicles are at the depot. Each customer has to be visited precisely once. Each
vehicle needs a route that takes it from the depot and back to the depot. What is the least cost
(distance/fuel/vehicles) solution?
Several vehicles normally needed, since (e.g. visiting 3 or 4 customers may take the whole
day, but all customers need to be serviced today. Also, vehicles have capacity constraints.
2-opt mutation for TSP/VRP etc…
Choose two edges at random
2-opt mutation for TSP/VRP etc…
Choose two edges at random
2-opt mutation for TSP/VRP etc…
Remove them
2-opt mutation for TSP/VRP etc…
Reconnect in a different way (there is only one valid new way)
“2-Opt heuristic”
2-opt turns out to be an excellent mutation operator for the TSP
and similar problems.
The “2-opt heuristic” is an algorithm based on it. There are variants,
But it comes down to Hillclimbing for n trials, until no further
improvement its possible, using 2-opt as the mutation operator.
Bullnheimer et al (97) Ant System for the VRP
The main difference
Standard transition rule
Extra pheromone is laid by
the ant with best tour
Benchmark problems
Deviation from best-known solution (smaller is better)
Run times
AS (ant) is pretty near state of the art, with good run times.
The [rectangular] Cutting Stock problem
You have a number of shapes to cut out from a large sheet.
How to do it, so as to minimise waste?
The [rectangular] Cutting Stock problem
You have a number of shapes to cut out from a large sheet.
How to do it, so as to minimise waste?