Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Job shop scheduling by simulated annealing
Operations Research
Applying tabu search to the job-shop scheduling problem
Annals of Operations Research - Special issue on Tabu search
Artificial fishes: physics, locomotion, perception, behavior
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
Ant algorithms for discrete optimization
Artificial Life
From Natural to Artificial Swarm Intelligence
From Natural to Artificial Swarm Intelligence
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Particle systems—a technique for modeling a class of fuzzy objects
SIGGRAPH '83 Proceedings of the 10th annual conference on Computer graphics and interactive techniques
Extending self-organizing particle systems to problem solving
Artificial Life
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Performance of digital pheromones for swarming vehicle control
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A Mathematical Theory of Communication
A Mathematical Theory of Communication
A flocking based algorithm for document clustering analysis
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Nature-inspired applications and systems
Collective-movement teams for cooperative problem solving
Integrated Computer-Aided Engineering - Performance Metrics for Intelligent Systems
Ants and reinforcement learning: a case study in routing in dynamic networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Enhanced probabilistic neural network with local decision circles: A robust classifier
Integrated Computer-Aided Engineering
Optimising operational costs using Soft Computing techniques
Integrated Computer-Aided Engineering
Causally-guided evolutionary optimization and its application to antenna array design
Integrated Computer-Aided Engineering
A computational intelligence optimization algorithm: Cloud drops algorithm
Integrated Computer-Aided Engineering
Hi-index | 0.00 |
Particle systems, a type of swarm intelligence system, have repeatedly been shown to be capable of more general problem solving than just collective movements. The emerging, self-organizing behavior of such systems leads to collective behavior that tends to be far more complex than that of their parts, yet at the same time their self-organizing nature typically makes their behavior difficult to predict and control. In previous work, we introduced a set of mechanisms to guide the self-organizing process, allowing the system designer to exert a form of high-level control over a self-organizing system. Here we extend these past results by incorporating a "pollen-based" distributed learning algorithm that increases the capabilities of a team of cooperating agents in pursuit of a global goal, while still retaining most of the simplicity of particle systems. To demonstrate these ideas, we use a dynamic logistics problem that combines the coordination and cooperation issues of collective transport with the global optimization difficulties of routing and shop-floor scheduling problems. The results show that this form of dynamic distributed learning enables a particle system to function effectively and to adapt to changing conditions relatively quickly. Further, the results suggest that the combination of distributed learning with collective movements provides an additional advantage that significantly affects system-wide behavior.