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Surrogate-based optimization and nature-inspired metaheuristics have become the state of the art in solving real-world optimization problems. Still, it is difficult for beginners and even experts to get an overview that explains their advantages in comparison to the large number of available methods in the scope of continuous optimization. Available taxonomies lack the integration of surrogate-based approaches and thus their embedding in the larger context of this broad field.
This article presents a taxonomy of the field, which further matches the idea of nature-inspired algorithms, as it is based on the human behavior in path finding. Intuitive analogies make it easy to conceive the most basic principles of the search algorithms, even for beginners and non-experts in this area of research. However, this scheme does not oversimplify the high complexity of the different algorithms, as the class identifier only defines a descriptive meta-level of the algorithm search strategies. The taxonomy was established by exploring and matching algorithm schemes, extracting similarities and differences, and creating a set of classification indicators to distinguish between five distinct classes. In practice, this taxonomy allows recommendations for the applicability of the corresponding algorithms and helps developers trying to create or improve their own algorithms.
The availability of several CPU cores on current computers enables
parallelization and increases the computational power significantly.
Optimization algorithms have to be adapted to exploit these highly
parallelized systems and evaluate multiple candidate solutions in
each iteration. This issue is especially challenging for expensive
optimization problems, where surrogate models are employed to
reduce the load of objective function evaluations.
This paper compares different approaches for surrogate modelbased
optimization in parallel environments. Additionally, an easy
to use method, which was developed for an industrial project, is
proposed. All described algorithms are tested with a variety of
standard benchmark functions. Furthermore, they are applied to
a real-world engineering problem, the electrostatic precipitator
problem. Expensive computational fluid dynamics simulations are
required to estimate the performance of the precipitator. The task
is to optimize a gas-distribution system so that a desired velocity
distribution is achieved for the gas flow throughout the precipitator.
The vast amount of possible configurations leads to a complex
discrete valued optimization problem. The experiments indicate
that a hybrid approach works best, which proposes candidate solutions
based on different surrogate model-based infill criteria and
evolutionary operators.