ant colony
DESCRIPTION
Ants are coolTRANSCRIPT
SOFSEM 2008
A Sensitive Metaheuristic for Solving a
Large Optimization Problem
Camelia-M. Pintea,Camelia Chira, D. Dumitrescu and Petrica C. Pop
Babes-Bolyai University and North University Romania
Outline Stigmergy Ant Colony Systems Autonomous Robots Sensitive Robots Drilling Problem Sensitive Robot Metaheuristic Numerical experiments and Statistical analysis Conclusions and further work
Stigmergy Collective behaviour of social individuals Indirect interactions
an individual modifies the environment other individuals respond to that change at a later
time
The environment mediates the communication among individuals
Self-organization stigmergic interactions
Stigmergy – ant systems
Ant System Ant System - proposed by M. Dorigo
(1992) Initially used for routing problems Successfully applied now to a broad
range of problems: Quadratic Assignment Problem, Scheduling problems, Recognizing Hamiltonian graphs, Dynamic graph search
Ants lay down pheromones as they travel Experiments show that pheromone builds
up more quickly on shorter paths An optimal path should be the one with
the strongest pheromone concentration after a certain amount of time
Basic concepts of Ant SystemKey concepts
• Cooperative behavior -ant algorithms make use of the simultaneous exploration of different solutions• Positive feedback -build a solution using local solutions, by keeping good solutions in memory• Negative feedback -to avoid premature convergence - evaporate the pheromone • Time scale -number of runs is critical• Stagnation -avoid good, but not very good solutions from becoming reinforced• Stigmergy -the indirectly communication between agents using pheromones
Cooperative behavior
Positive feedback
Negativefeedback Time scale Stagnation Stigmergy
Leonel Moura + Vitorino Ramos, 2002
A B
Ant Colony Systems (ACS) Systems based on agents Inspiration: behavior of real ant colonies
- Ants deposit on ground pheromone (while walking between food sources and nest) and can smell pheromone- Ants tend to choose strong pheromone trails
Ant Colony Optimization Path followed by an ant: candidate solution Ants deposit pheromone along the path followed
proportional to the quality of corresponding candidate solution
Paths with stronger pheromone trails are preferred
ACO metaheuristic robust and versatile Successfully applied to a range of CO problems
Stigmergy and Autonomous Robots
No global plans
Bonabeau, E. et al.: Swarm intelligence from natural to artificial systems. Oxford, UK.
Stigmergy provides a general mechanism that relates individual and colony level behaviors
The behavior-based approach to design intelligent systems has produced promising results in a wide variety of areas: military applications, mining, space exploration, agriculture, factory automation, service industries, waste management, health care and disaster intervention.
Autonomous robots can accomplish real-world tasks without being told exactly how.
Sensitive Robots Artificial entities with a Stigmergic Sensitivity Level (SSL) expressed by a
real number in the unit interval [0, 1]. Robots with small SSL values
highly independent environment explorers potential to autonomously discover new promising regions of the search space search diversification can be sustained.
Robots with high SSL values intensively exploit the promising search regions already identified the robot behavior emphasizes search intensification
The SSL value can increase or decrease according to the search space topology encoded in the robot experience.
Sensitive Robot Metaheuristic (SRM) Combines stigmergic communication and
autonomous robot search Qualitative stigmergic mechanism “Micro-rules” define action-stimuli pair for a
robot
SRM for solving a Large Drilling problem SRM implemented using two teams of
robots1. First team of robots with small SSL values
Small SSL-robots (sSSL robots) Sensitive-explorer robots Search diversification
2. Second team of robots with high SSL values High SSL-robots (hSSL robots) Sensitive-exploiter robots Search intensification
Problem
Drilling Problem The process of manufacturing the printed circuit board (PCB) is
difficult and complex. Drilling small holes require precision and is done with the use of
an automated drilling machine driven by computer programs. The large drilling problem is a particular class of Generalized
Traveling Salesman Problem involving a large graph and finding the minimal tour for drilling on a large-scale PCB
The Generalized Traveling Salesman Problem (GTSP)
Introduced by Laporte and Nobert in 1983 and Noon and Bean in 1991
Applications to location and telecommunication problems
C-M. Pintea, C.P. Pop, C. Chira: The Generalized Traveling Salesman Problem solved with Ant Algorithms (ACS for GTSP from numerical experiments) J.UCS, in press, 2008
A graphic representation of the Generalized Traveling Salesman problem solved with ant system.
• Nodes of complete undirected graph clustered• Find a minimum-cost tour passing through exactly one node from each cluster
Sensitive Robot Metaheuristic (SRM) for Large Drilling problem
SRM model relies on the reaction of virtual sensitive robots to different stigmergic variables
Each robot is endowed with a particular stigmergic sensitivity level to ensure a good balance between search diversification and intensification
Sensitive Robot Algorithm
Numerical experiments (1)
[1] Bixby, B., Reinelt, G.: http://nhse.cs.rice.edu/softlib/catalog/tsplib.html (1995)
Comparisons Nearest Neighbor (NN)
Rule: always go next to the nearest as-yet-unvisited location
GI3 composite heuristic Construction of an initial partial solution Insertion of a node from each non-visited node subset Solution improvement phase
Random Key Genetic Algorithm Combines GA with a local tour improvement heuristic Solutions encoded using random keys
ACS for GTSP
Numerical experiments (2)
[8] Renaud, J., Boctor, F.F.: An efficient composite heuristic for the Symmetric Generalized Traveling Salesman Problem. Euro. J. Oper.Res., (1998)[9]. Snyder, L.V., Daskin, M.S.: A Random-Key Genetic Algorithm for the Generalized Traveling Salesman Problem. INFORMS, San Antonio, TX (2000).
Statistical analysis The Expected Utility Approach technique has been employed to determine
the accuracy of each heuristic
• SRM has Rank 1 being the most accurate algorithm within the compared set of algorithms
Conclusions and further work Bio-inspired robot-based model for complex
travel robotic problems Potential Improvements
Execution time Parameter values Efficient combination with other algorithms
Future Work Variable SSL - learning Numerical experiments - NP-hard problems Search and optimization in dynamic complex
networks
Optimal Route
Actual Route
