Swarm Intelligence

MindSet-Optimizer implements advanced Computational and Swarm Intelligence algorithms to perform an efficient analysis of the problem to be optimized. For a given cost function, the Optimizer adjusts system parameters, in order to optimize model values. The input objective function may be either a mathematical model or a code-generated value.

No Equations Needed

No need of mathematical modelling is required. Optimizer's User Objective function only needs to be able to return a numeric value for given inputs. Therefore, the user can enter a simulation or data analysis procedure as input.


Optimizer features a user-friendly GUI, allowing for model development in Python, or access via API from .Net or SQL. Optimizer may be added to an existing .Net project, enabling calibration of multi-parameter problems.

Main Features

Solve Optimization Problems

Minimizes \ Maximizes an objective function, given constraints and penalty function. Problems may be non-linear or multidimensional (up to dozens of variables).

User-friendly interface

Intuitive GUI for definition of optimization problems. Problem definition (along with any solution found) can be saved to a file, allowing for quick future reference

Model Definition

Preformed in Python language used in GUI interface. User may also define models in .Net via API.

.Net Connectivity

Use API to call for optimization from C# or other .Net language.

Uses Known Algorithms

Particle Swarm Optimization, Gravitational Search Algorithm, and Simulated Annealing are used to solve optimization problems.

Deploy Applications with Optimization

The Optimizer component may be incorporated in existing .Net projects and systems.

Scientific Background

Optimizer algorithms simulate a virtual particle swarm, which moves through the search space defined by the optimization variables. In their movement, the particles interact with one another, sharing information regarding the various solutions found. As each particle seeks to find its best individual solution, the entire swarm advances toward finding the system's optimal solution.

The advanced features of Optimizer's implemented algorithms extend beyond those known in literature.

Description of Algorithms



Optimizer doesn't require a mathematical model. As long as a numerical output is generated, the model can serve as an objective function.
Yes, Optimizer features both GUI based and .Net DLL forms.
Yes, Optimizer may be added to your applications. This allows you to solve optimization problems dynamically.