ant colony optimization models in constraint …€¦ · ant colony optimization models in...
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Basque Center for Applied Mathematics (BCAM)TECNALIA
ANT COLONY OPTIMIZATION MODELS
IN CONSTRAINT SATISFACTION-BASED
APPLICATIONSAntonio González Pardo
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Table of contents
Introduction
State of the art
Modelling Complex Problems based on Restricted
Graphs
A New ACO model for complex graph-based problems
Experimental Phase
Conclusions
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Introduction
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Motivation
There exists a wide number of problems of special interest
in the research community and the industry.
Classical problems: Travelling Salesman Problem, Graph
colouring problem, knapsack problem
Industrial problems: Project Scheduling Problem, Car
Sequencing, Vehicle Routing Problem, Video Games
All of them are NP-complete: not solvable in realistic time.
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Computational Intelligence
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Swarm Intelligence
Originated from the study of colonies of social organisms.
Individuals are simple, but the swarm generate complex behaviour based on the pattern interactions.
The communication can be direct (by physical contact) or indirect (by changing the environment)
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State of the art
Bio-Inspired Algorithms
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Ant Colony Optimization
The first ACO model was proposed by Marco Dorigo in
1992.
The ACO algorithm is based on the foraging behaviour of
the ants.
Traditionally, ACO is executed on a graph, G=(E,V), where the goal of the ants is to find the shortest path
connecting two given nodes that represent the nest and
the food source.
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Ant Colony Optimization
Ants must select the next node to visit and they deposit
on the edges the different pheromones.
Once they finish the execution, the ants go back to the
nest depositing some pheromones that contains
information about the built solution.
The pheromones influence the decision of other ants.
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Ant Colony Optimization
Ants select the next node to visit using the following
equation:
There is an evaporation process that reduces the
quantity of pheromones to avoid ants to follow bad
paths (solutions)
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Coral Reef Optimization
CRO algorithms are based on the formation, and
reproduction of reef.
Corals are located in the reef but the space is a limited
resource against the high reproduction rate of the corals.
Corals must fight to obtain a place in the reef.
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Coral Reef Optimization
The reef is defined by a set of corals located in an NxM
square grid.
Each coral represents a possible solution to the problem.
There is, also, a function that measures the health, or
goodness, of each coral.
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Coral Reef Optimization
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Coral Reef Optimization
The corals evolve generating new corals from the ones
located in the reef. There are three different reproduction
procedures:
Broadcast Spawning: two corals generates a new coral
larva.
Brooding consists on the generation of a new larva by
copying and mutating an existing coral.
Budding: the best corals are duplicated.
Larvae setting is the process by which new corals can
find a place into the reef.
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State of the art
Examples of CSP problems
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CSP-based Problems
Constraint Satisfaction Problems are NP-hard problems
composed by a set of variables (X), that must a be
assigned a set of values (D), whereas a set of constraints
(C) must be satisfied.
There is a wide number of complex research and
industrial problems that can be modelled using CSP.
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N-Queens Problem
The goal is to place N different queens in a NxNchessboard in such a way queens do not attack each
other, and there are not more than one queen in each
square.
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Resource-Constrained Project
Scheduling Problem (RCPSP)
There is a project composed by a set of activities that need to be processed.
Each activity can be executed in one, or more, execution modes. Modes differ in the resources, and time, needed to complete the activity.
There are precedence constraint and resources constraints.
The goal is to find a schedule for the project with the minimum makespan (i.e. the duration of the whole project) whereas all the constraints are satisfied.
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Modelling Complex Problems based
on Restricted GraphConstraint Satisfaction Problems
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Graph-based Modelling in ACO
For executing ACO algorithms, a decision graph is
needed.
Constraint Satisfaction Problems (CSP) are representedby a highly connected decision graph where there areas many nodes as <variable,value> pairs.
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Representation of the N-Queens
Problem
Modelling the N-Queens Problem as a CSP:
Each queen is defined by the x and y coordinates that
identify the position of the queen.
Each variable can be assigned N different values in a NxNchessboard.
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Representation of the N-Queens
Problem
[Solnon,2002] proposed the creation of a graph, G=(E,V):
Number of nodes:
Number of edges:
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Representation of the N-Queens
Problem
[Khan et al. , 2009] uses a different representation.
The graph contains a layer for each queen in the problem.
Each layer contains a node for each square in the chessboard.
Nodes in layer i are connected to the nodes of layer (i+1)
Number of nodes:
Number of edges:
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Representation of the N-Queens
Problem
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A new ACO model for complex
graph-based problems
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A new ACO model
The new ACO model proposed in this work is composed
by:
The decision graph for complex problems
The ants behavior
The Oblivion Rate metaheuristic
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A new ACO model for complex
graph-based problemsThe new decision graph for ACO algorithms
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A new decision graph for ACO
algorithms
The new decision graph is smaller than the ones created
in the classical approaches.
The proposed representation creates a node for each
given variable in the problem, independently of the
values that can be assigned.
The edges of the graph will connect those nodes whose
values are restricted by any constraint in the problem.
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A new decision graph for N-Queens
Problem
Each variable of the problem is a Queen, and the task of
assigning a value for each queen is traduced into
assigning a value for its both coordinates, x and y.
Given a problem with N Queens, the decision graph is
composed by N nodes.
The restrictions involve all the queens available in the
problem, so the graph is full connected:
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A new decision graph for N-Queens
Problem
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A new decision graph for RCPSP
Each node of the decision graph represent each job that contained the project
To create the edges, we start using the Activity-on-Node network.
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A new decision graph for RCPSP
Problems identified with AoN:
Parallelism is not allowed.
Some sequential executions are not represented.
Solution: two nodes (i,j) are connected:
if both nodes are connected in the AoN.
if activity j is neither a direct successor nor an indirect
successor of the activity i
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A new ACO model for complex
graph-based problemsThe new ants behaviour
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A new ant behaviour
Using the standard approaches, the ants select the next
node to visit.
The node contains the assignation of the value.
Ants have to update their local solution and place some
pheromones in the graph.
With the proposed approach the value is not encoded
into the node, so the behaviour of the ants is more
complex.
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A new ant behaviour
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A new ACO model for complex
graph-based problemsThe Oblivion Rate metaheuristic
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The Oblivion Rate
The complexity derived from the number of nodes in the classical approach is converted into a fast growth in the number of pheromones created in the graph.
Depending on the problem, it could be that the number of pheromones in the graph saturates the system.
The Oblivion Rate metaheuristic is included in the model to control the number of pheromones created in the graph.
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The Oblivion Rate
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The Oblivion Rate
Dynamic Oblivion Rate takes into account the saturation
of the system using the number of pheromones created
in the system.
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The Oblivion Rate
CRO-based Oblivion where:
A coral A pheromone
The health of the coral The pheromone value
Replica control and reproduction procedures, are not
needed.
Larvae setting and depredation process are used to
remove corals (pheromones) from the model.
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Experimental Phase
N-Queens Problem
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Experimental Phase: N-Queens
This first application domain was used to perform an initial
study about the behaviour of the proposed model.
Three different questions:
Could the standard Evaporation Process work as the
Oblivion Rate?
Is the Oblivion Rate needed?
Does this approach work?
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Experimental Phase: N-Queens
Could the standard Evaporation Process work as the
Oblivion Rate?
100 ants try to solve the 25-
Queens Problem during 200
steps.
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Experimental Phase: N-Queens
Is the Oblivion Rate needed?
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Experimental Phase: N-Queens
Does this approach work?
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Experimental Phase
Resource-Constraint Project Scheduling Problem
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Experimental Phase: RCPSP
The problems analysed have been extracted from
PSPLib.
It offers a large set of problems with different characteristics.
The optimal solution, or the best-known heuristic value, for
each problem is provided.
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Experimental Phase: RCPSP
Experiment #1: compare the reduction percentage of
the Dynamic Oblivion Rate against the Normal ACO.
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Experimental Phase: RCPSP
Experiment #2: check whether the reduction in the
number of pheromones affects to the quality of the
solutions found by the ACO algorithm.
We introduce the CRO-based Oblivion Rate.
The size of the reef is determined by the maximum number of pheromones created in the system using the
Dynamic Oblivion Rate.
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Experimental Phase: RCPSP
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Experimental Phase: RCPSP
Experiment #3: comparison to the State of the Art.
Only j120.sm dataset is analyzed.
Stopping criteria is the number of schedules (solutions)
found.
Measurement: average deviation (%) from the critical
path lower bound.
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Experimental Phase: RCPSP
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Conclusions
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Conclusions
A new ACO model that uses a reduced decision graph has been proposed.
The simplification of the graph affects to: The ants behaviour that becomes more complex.
A metaheuristic to control the number of pheromones (called Oblivion Rate)
Two different Oblivion Rate metaheuristics have been analyzed: Dynamic and CRO-based.
The proposed model outperforms the results from the literature for the most complex problem and it reduces drastically the number of pheromones created.
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Basque Center for Applied Mathematics (BCAM)TECNALIA
ANT COLONY OPTIMIZATION MODELS
IN CONSTRAINT SATISFACTION-BASED
APPLICATIONSAntonio González Pardo
Thank you for your attention.