MEIGOR

MEIGOR implements metaheuristic global optimization algorithms for solving continuous nonlinear (cNLP), mixed-integer nonlinear (MINLP) and integer programming (IP) problems and for Bayesian parameter estimation in systems biology and bioinformatics.

Key Features:

• Metaheuristic Algorithms: Includes Enhanced Scatter Search (eSS) for continuous nonlinear programming (cNLP) and mixed-integer nonlinear programming (MINLP) and Variable Neighborhood Search (VNS) for Integer Programming (IP).
• Parallel Processing: Supports single-threaded and parallel execution using a cooperative parallel strategy for eSS and VNS.
• Bayesian Inference (BayesFit): Provides BayesFit in the R implementation for Bayesian parameter estimation.
• Multi-Language Support: Implemented in R and Matlab with Python access available via an R wrapper.
• Modular Architecture: Modular design enabling integration of additional optimization methods.

Scientific Applications:

• Parameter Estimation: Bayesian inference via BayesFit for parameter estimation of biological models.
• Optimization Benchmarks: Applied to optimization benchmarks to evaluate performance in global optimization tasks relevant to systems biology and bioinformatics.

Methodology:

Uses Enhanced Scatter Search (eSS) and Variable Neighborhood Search (VNS) metaheuristics; employs a cooperative strategy for parallel processing to enable simultaneous exploration of solution spaces; provides BayesFit for Bayesian parameter estimation; implemented in R and Matlab with Python access via an R wrapper.