Central Force Optimization

Central Force Optimizatoin (CFO) is a deterministic, population-based metaheuristic algorithm. Over the years I've worked with it in various forms, extended it, and published multiple papers with regards to the algorithm. All of the code that I am currently maintaining can be found using the following list:

Particle Swarm Optimization

Particle Swarm Optimization (PSO) is an extremely popular and simple Population-based Metaheuristic based on the principles of flocking and swarming. Over the years I've used it in a variety of contexts and written a large amount of code relating to it.

Power System Reliability

Power system reliability using non-sequential Monte Carlo Simulation (MCS) is an interesting problem. This software relates primarily to my dissertation and continues to be extended.


This software is a cloud file storage aggregator that provides a high level of security as 1) Users are required to offer no personal information and 2) All data is encrypted and split into pieces for secure storage across multiple services.

Computational Intelligence and the Smart Grid

  • Python Intrusion Detection. Developed in conjunction with the University of Applied Sciences in Salzburg, Austria, this software is used to test a classifier voting mechanism for detecting cyber-security intrusions in the Smart Grid using Computational Intelligence techniques. The KDD-NSL and KDD-Cup 1999 Datasets are the provided features to train and test different types of classifiers. The software allows for the implementation and evaluation of various intrusion detection algorithms using the ensemble methodology.
  • Okeanos. Particularly with respect to coordinating power consumption and generation, demand response (DR) is a vital part of the future smart grid. Even though, there are some DR simulation platforms available, none makes use of game theory. Okeanos is a fundamental, game theoretic, Java-based, multi-agent software framework for DR simulation that allows an evaluation of real-world use cases. While initial use cases are based on game theoretic algorithms and focus on consumption devices only, further use cases evaluate the effects of plug in electric vehicles (PEVs). Results with consumers show that the number of involved households does not affect the costs per household. Further evaluation involving PEVs demonstrates that with an increasing penetration of PEVs and feed-in tariffs the costs per household per month decrease.

Imbalanced Data and Machine Learning

Cloud Computing Reliability

Cloud Computing Oversubscription

GSO Registration Application