Optimization Algorithms | www.esteco.com

Optimization Algorithms

Find better design solutions, faster with a comprehensive collection of optimization algorithms, specially designed for engineering applications.

Handle hundreds of design parameters simultaneously, balance complex tradeoffs and quickly identify a set of optimal solutions, even for the most difficult design problems.

 

Meet contrasting objectives and improve your product performance.

Our smart, self-learning algorithms will guide you to optimal solutions in less time and with less computational resources.

Pick the right technique to tackle every engineering challenge.

Choose among a complete set of numerical tools covering deterministic, stochastic and heuristic methods for both single and multi-objective problems.

Fully exploit your expertise and resources.

Choose your set-up approach and define an efficient investigation of the design space, based on the amount of time available and the level of expertise.

 

different set-up MODES
AUTONOMOUS

Seek optimum solution while minimizing the number of iterations required. With no parameter settings, the algorithm smartly uses the information gathered from the problem analysis and stops when there is no further improvement. 

SELF-INITIALIZING

Meet tight deadlines by rapidly identifying promising design solutions when working with time constraints. By choosing only the number of evaluations, teams can bring their optimization projects on the fast track.

MANUAL

Fully exploit your optimization expertise and your deep knowledge of the engineering problem at hand. Build your own optimization strategy, by setting all the underlying parameters.

FOCUS ON MULTI-STRATEGY ALGORiTHMS

 

Combine powerful optimization methods to further reduce time and effort. 

Besides the traditional methods (Heuristic, Derivative free, Gradient-Based), our numerical technology offers fine-tuned multi-strategy optimization algorithms able to multiply the capabilities of single approaches. 

MEGO achieves fast convergence rate and robustness in the case of optimization problems with many local optimal solutions, finding the global optimum by providing an automatic trade-off between exploration and exploitation.

pilOPT encloses multiple numerical investigation strategies to offer a smart exploration of the design space, making the best out of time and computational resources available by exploiting the internal adaptive algorithm strategy.

FAST accelerates the process by exploiting RSM performance over the region of most interest in the design space, reaching a high-speed detection of the optimal solutions.

HYBRID automatically combines the robustness of genetic algorithms with the accuracy of gradient methods, providing an unprecedented balance between exploration and refinement capabilities.

SAnGeA provides an automatic screening phase, coupled with a genetic algorithms global search phase, reliably identifying the most meaningful variables to face high-dimension and unconstrained problems.