How islam4562/the-main-characteristics-of-swarm-intelligence works
This repository is a collection of runnable swarm-intelligence examples, not a shared application. The principal workflow is to run an example, let its agents or optimizer perform repeated local decisions and updates, then inspect an optimized result or visualization. The README also presents five SI characteristics; the corresponding ant-colony examples illustrate those ideas, while bee search, particle-swarm tuning, and Q-learning broaden the demonstrations. Source excerpts support the shown algorithm and output links; the unsampled ACO hyperparameter script is included as a documented capability, with its CSV input indicated by its source code.
Main entry points: aco_hyperaram.py ACO Hyperparameter Search · bee_alg.py Bee Algorithm · self_organization.py Self Organization · pso_metro.py Metro Route ACO
Components 20
SI Characteristics
- Emergent Behavior
emerging_behavior.py - Self Organization
self_organization.py - Adaptation
adaptation.py - Decentralized Control
decentralized_management.py - Scalability
scalability.py - Pheromone Plot
emerging_behavior.py - Training Plots
self_organization.py - Pheromone Plot
adaptation.py - Pheromone Plot
decentralized_management.py - Colony Plot
scalability.py
Ant Colony Examples
- Metro Route ACO
PSO Metro/pso_metro.py - Animated Route ACO
PSO Metro/pso_metro_animation.py - ACO Hyperparameter Search
ACO Hyperparameter/aco_hyperaram.py - Hyperparameter Data
ACO Hyperparameter/hyperparameter_data.csv
Swarm Optimizers
- Bee Algorithm
Bee Algorithm/bee_alg.py - Bee Data
Bee Algorithm/ba_data.csv - Fish PSO Tuning
PSO example fish/pso_fish.py
Learning Examples
- Grid Environment
self_organization.py - Q Learning
self_organization.py
Other components
- Engineer
Connections 24
- Engineer → Emergent Behavior: runs example
- Engineer → Self Organization: runs example
- Engineer → Adaptation: runs example
- Engineer → Decentralized Control: runs example
- Engineer → Scalability: runs example
- Engineer → Metro Route ACO: runs example
- Engineer → Animated Route ACO: runs example
- Engineer → ACO Hyperparameter Search: runs example
- Engineer → Bee Algorithm: runs example
- Engineer → Fish PSO Tuning: runs example
- Emergent Behavior → Pheromone Plot: plots pheromones
- Self Organization → Grid Environment: steps environment
- Q Learning → Grid Environment: chooses actions
- Self Organization → Training Plots: plots rewards
- Adaptation → Pheromone Plot: plots pheromones
- Decentralized Control → Pheromone Plot: plots pheromones
- Scalability → Colony Plot: plots colony
- Metro Route ACO → Metro Route ACO: updates pheromones
- Animated Route ACO → Animated Route ACO: animates routes
- ACO Hyperparameter Search → Hyperparameter Data: reads data
- ACO Hyperparameter Search → ACO Hyperparameter Search: evaluates candidates
- Bee Algorithm → Bee Data: reads data
- Bee Algorithm → Bee Algorithm: ranks solutions
- Fish PSO Tuning → Fish PSO Tuning: evaluates particles