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Adham Atyabi Somnuk Phon-Amnuaisuk, Chin Kuan Ho Multimedia University, Malaysia Paper Accepted at IEEE Congress on Evolutionary Computation (CEC 2008), Hong Kong, June 1-6 2008. Cooperative Learning of Homogeneous and Heterogeneous Particles in Area-extension PSO. The environment PSO

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Adham Atyabi

Somnuk Phon-Amnuaisuk, Chin Kuan Ho

Multimedia University, Malaysia

Paper Accepted at IEEE Congress on Evolutionary Computation (CEC 2008), Hong Kong, June 1-6 2008

Cooperative Learning of Homogeneous and Heterogeneous Particles in Area-extension PSO

IEEE Congress on Evolutionary Computation CEC2008


The environment

PSO

Area-extension PSO

Learning in PSO

Results and conclusion

Outline

IEEE Congress on Evolutionary Computation CEC2008


The environment is a hostile robotic scenario based on cooperative robots trying to locate bombs and disarm them.

The robots know the likelihood of having bombs in the area, but not their precise locations.

The likelihood information could be uncertain (because of noise).

Simulated Environment

IEEE Congress on Evolutionary Computation CEC2008

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IEEE Congress on Evolutionary Computation CEC2008 cooperative robots trying to locate bombs and disarm them.


Plausible applications: applying PSO to navigate agents in uncertain environments

IEEE Congress on Evolutionary Computation CEC2008


PSO is an Evolutionary Algorithm (EA) inspired from animal social behaviors. (Kennedy, Eberhart, 1995)

The method was inspired by the movement of flocking birds and their interactions with their neighbors in the group.

EA achieves optimization using three primary principles:

Evaluation, where quantitative fitness can be determined for each agent (particle);

Comparison, where the best performer among agents can be selected;

Imitation, where the qualities of better agents are mimicked by others.

Particle Swarm Optimisation

IEEE Congress on Evolutionary Computation CEC2008


Every particle in social behaviors.the population begins with a randomized position Xij and randomized velocity Vij in the n-dimensional search space, where i represent the particle index and j represents the dimension in the search space

Each particle remembers the position at which it achieved its highest performance (p).

Each particle is also a member of some neighborhood of particles, and remembers which particle achieved the best overall position in that neighborhood (g).

Vij(t) = Last Velocity + Cognitive component +

Social component

Vij(t) = w*Vij(t-1)+C1*R1*(pij-Xij(t-1))+C2*R2*(gi-Xij(t-1))

Xij(t) = Xij(t-1) + Vij(t)

Update equations

IEEE Congress on Evolutionary Computation CEC2008


Trajectory of five simulated agents navigated using basic pso
Trajectory of five simulated agents navigated using basic PSO

IEEE Congress on Evolutionary Computation CEC2008


On a good day
On a good day PSO

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Points to ponder PSO

Particles seem to stick in one place. This results in ineffective exploitation and exploration.

How should we inform the swarm about fruitful positions? (without giving away the solution).

How should the swarm communicate/share useful information?

There is no free lunch.

Basic PSO does not seem to work. Why?

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The idea is based on dividing the environment to sub virtual fixed areas with various credits.

Credit in the area is defined as the proportion of goals and obstacles positioned in the area.

Particles know the credit of the first and second layer of its current neighborhood.

Area-Extension PSO

IEEE Congress on Evolutionary Computation CEC2008


New velocity update rules. virtual fixed areas with various credits.

Help Request Signal which provide cooperation between different sub-swarms.

Reward and penalty of their actions which are used in the controls of Leave Force and Speculation mechanisms.

Leave Force and Speculation mechanisms help prevent particles from over-exploring unfruitful areas.

Heuristics to guide search

IEEE Congress on Evolutionary Computation CEC2008


Velocity update rules virtual fixed areas with various credits.

IEEE Congress on Evolutionary Computation CEC2008


Communication among particles virtual fixed areas with various credits.

  • Particles can only communicate with those who are in their communication range.

  • Various communication ranges are used (500, 250, 125, 5 pixels).

  • This heuristic has a major effect on the sub swarm size.

  • Help request signal can provide a chain of connections.

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More knowledge virtual fixed areas with various credits.

IEEE Congress on Evolutionary Computation CEC2008


Fitness of PSO, P(.) and G(.) virtual fixed areas with various credits.

  • The fitness of a particle is derived from the number of bombs in its observation.

  • Local best P(.) and global best G(.) are derived from the fitness values according to its own observation and from observations shared from other particles.

  • Movements of AEPSO is derived from both velocity update rules and informed fitness derived from hot zone.

  • Movement of cooperative learning AEPSO is derived from velocity update rules hot zone and effective direction learned from experience.

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Movement Trajectories (homogeneous) virtual fixed areas with various credits.

IEEE Congress on Evolutionary Computation CEC2008


Aepso vs random search and linear search
AEPSO vs. Random Search and Linear Search virtual fixed areas with various credits.

IEEE Congress on Evolutionary Computation CEC2008


Area-Extension-Cooperative PSO virtual fixed areas with various credits.

  • Particles incorporate knowledge from their training session.

  • Particles share their priority areas with others.

  • In homogeneous PSO, all particles are considered to have the same properties.

  • In heterogeneous PSO, particles do not have the same properties. In the experiment, this is translated to the environments with different bomb types which require different particles (types of robots to disarm them).

IEEE Congress on Evolutionary Computation CEC2008


The aim is to learn virtual fixed areas with various credits.

(a) which is the best area?

(b) what decision is made? and

(c) is it the right decision?

In the training phase, the training method could be either Individual training or Team- based training.

Initialization in the testing phase may be either with the same initialization as the training or with different initialization.

Learning in AEPSO

IEEE Congress on Evolutionary Computation CEC2008


More knowledge virtual fixed areas with various credits.

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Simulation results virtual fixed areas with various credits.

IEEE Congress on Evolutionary Computation CEC2008


Homogeneous virtual fixed areas with various credits.# bomb explosions

The results are from 20 runs (each run is 20,000 iteration). In each run, 5 robots, 51 bombs, and 51 obstacles are used (table IV).

IEEE Congress on Evolutionary Computation CEC2008


Homogeneous virtual fixed areas with various credits.# disarmed bombs

IEEE Congress on Evolutionary Computation CEC2008


Heterogeneous virtual fixed areas with various credits.# bomb explosions

IEEE Congress on Evolutionary Computation CEC2008


Heterogeneous virtual fixed areas with various credits.# disarmed bombs

IEEE Congress on Evolutionary Computation CEC2008


Heterogeneous AEPSO virtual fixed areas with various credits.Movement trajectory after learning

IEEE Congress on Evolutionary Computation CEC2008


Balancing between exploitation and exploration is the key to good performance.

No free lunch theory is true in general.

Enhancing PSO with extra knowledge may lead to useful applications in exploration tasks (e.g., sea bed exploration).

Conclusion

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THANK YOU good performance.Q&A

IEEE Congress on Evolutionary Computation CEC2008



Parameters setting good performance.

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Heterogeneous AEPSO good performance.Movement trajectory before learning

IEEE Congress on Evolutionary Computation CEC2008


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