The Advanced Metaheuristics Algorithms (AMA) Toolkit for LabVIEW is a suite of software tools, example programs, and utilities for global optimization problems and statistical analysis.

Applications of optimization are countless. Every process has a potential to be optimized. There is no company that is not involved in solving optimization problems. Indeed, many challenging applications in science and industry can be formulated as optimization problems. Optimization occurs in the minimization of time, cost, and risk or the maximization of profit, quality, and efficiency.

A large number of real-life optimization problems in science, engineering, economics, and business are complex and difficult to solve. They cannot be solved in an exact manner within a reasonable amount of time. Using approximate algorithms is
the main alternative to solve this class of problems.

Approximate algorithms can further be decomposed into two classes: specific heuristics and metaheuristics. Specific heuristics are problem dependent; they are designed and applicable to a particular problem. Metaheuristics represent more general approximate algorithms applicable to a large variety of optimization problems. They can be tailored to solve any optimization problem. Metaheuristics solve instances of problems that are believed to be hard in general, by exploring the usually large solution search space of these instances. These algorithms achieve this by reducing the effective size of the space and by exploring that space efficiently. Metaheuristics serve three main purposes: solving problems faster, solving large problems, and obtaining robust algorithms.

Knowing that in the time of releasing this toolkit, LabVIEW incorporates only Differential Evolution (DE) algorithm. This toolkit gives the abilaty to an engineer and/or a reasercher to start using advanced metaheuristics algorithms in solving hard optimization problems using LabVIEW.

This original LabVIEW implementation for advanced metaheuristic algorithms includes, in this first release, the techniques of perturbed Particle Swarm Optimization (pPSO), Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), Teaching-Learning Based Optimization (TLBO) and Grey Wolf Optimizer (GWO). 9 benchmarks of test functions from the optimization literature are also avalable in this toolkit for validation tests.

Requirements:
MathScript RT Module for Windows

Author note:
For any inquiries regarding the improvement of this release, please write to: mohamedlotfi.derouiche@enit.rnu.tn