RMOP has been designed to be Multi-Platform. It has been successfully tested in Windows, MAC/OS and Linux operating systems.

It uses evolutionary algorithms to solve optimization problems and it is able to find global extremum (maximum or minimum) even in complex functions with many local maxima or minima. RMOP is multi-objective by design. Depending on the problem to optimize, users can choose the number of objective functions that suit their specific needs. It uses a population based strategy, designers can choose the optimal individual(s) from the Pareto set. Results are presented in in a graphical and user-friendly way.

RMOP is able to manage an unlimited number of design variables. According to the problem to be optimized, RMOP provides an easy to use tool to manage the required information. For example, in a multi-element airfoil three design variables control the x-y position and rotation of the flap and another three design variables control the same parameters of the slat.

RMOP can deal with any physical (and multi-physical), mathematical or engineering problem. Engineers can select any parameter from the problem definition; from geometrical variables to restrictions to the solution. Furthermore, the objective function is also selected by the engineer using the system. Its meaning can be any measurable quantity such as lift and drag in aerodynamics, strength and weight in structural mechanics, springback in deep-drawing processes, energy consumption in any manufacturing process or monetary cost in any engineering process.

RMOP can be easily coupled with any solver. User can choose the solver of their preference depending on the problem to optimize or the level of detail of the calculations needed. For example: Computational Fluid Dynamics, Structural Mechanics … Additionally, RMOP has a C++ coupling environment to join RMOP with any solver in a uniform and platform-independent manner, saving time on the coupling process.

Parallel Computation RMOP is able to work in parallel in shared memory systems (desktop computers) or distributed memory systems (computing clusters). This offers a great advantage in terms of speed because many individuals of the population can be evaluated at the same time. This reduces drastically the wall time needed to solve an optimization problem, and use all the computational resources.