SwarmOpt is a library of swarm optimization algorithms implemented in Python.
Swarm intelligence leverages global population-based search solutions to balance exploration and exploitation with respect to specified cost functions. The PSO lineage was sparked by Eberhart and Kennedy in their original paper on PSOs in 1995, and the intervening years have seen many variations spring from their central idea.
To install SwarmpOpt, run this command in your terminal:
$ pip install swarmopt- Global Best PSO - Kennedy & Eberhart 1995
- Local Best PSO - Kennedy & Eberhart 1995
- Unified PSO - Parsopoulos & Vrahatis 2004
- Dynamic Multi-Swarm PSO - Liang & Suganthan 2005
- Simulated Annealing PSO - Mu, Cao, & Wang 2009
Single objective test functions:
- Sphere Function
- Rosenbrock's Function
- Ackley's Function
- Griewank's Function
- Rastrigin's Function
- Weierstrass Function
- Cooperative Approach to PSO (CPSO)(multiple collaborating swarms)
- Proactive Particles in Swarm Optimization (PPSO) (self-tuning swarms)
- Inertia weight variations
- Mutation operator variations
- Velocity clamping variations
- Multiobjective variations
- Benchmark on something canonical like MNIST
- Neural network number of layers and weight optimization
- Grid scheduling (load balancing)
- Routing in communication networks
- Anomaly detection
Siobhán K Cronin, SwarmOpt (2018), GitHub repository, https://github.com/SioKCronin/SwarmOpt