2,618 research outputs found
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
Constrained optimization of high-dimensional numerical problems plays an
important role in many scientific and industrial applications. Function
evaluations in many industrial applications are severely limited and no
analytical information about objective function and constraint functions is
available. For such expensive black-box optimization tasks, the constraint
optimization algorithm COBRA was proposed, making use of RBF surrogate modeling
for both the objective and the constraint functions. COBRA has shown remarkable
success in solving reliably complex benchmark problems in less than 500
function evaluations. Unfortunately, COBRA requires careful adjustment of
parameters in order to do so.
In this work we present a new self-adjusting algorithm SACOBRA, which is
based on COBRA and capable to achieve high-quality results with very few
function evaluations and no parameter tuning. It is shown with the help of
performance profiles on a set of benchmark problems (G-problems, MOPTA08) that
SACOBRA consistently outperforms any COBRA algorithm with fixed parameter
setting. We analyze the importance of the several new elements in SACOBRA and
find that each element of SACOBRA plays a role to boost up the overall
optimization performance. We discuss the reasons behind and get in this way a
better understanding of high-quality RBF surrogate modeling
Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution
It is not uncommon that meta-heuristic algorithms contain some intrinsic
parameters, the optimal configuration of which is crucial for achieving their
peak performance. However, evaluating the effectiveness of a configuration is
expensive, as it involves many costly runs of the target algorithm. Perhaps
surprisingly, it is possible to build a cheap-to-evaluate surrogate that models
the algorithm's empirical performance as a function of its parameters. Such
surrogates constitute an important building block for understanding algorithm
performance, algorithm portfolio/selection, and the automatic algorithm
configuration. In principle, many off-the-shelf machine learning techniques can
be used to build surrogates. In this paper, we take the differential evolution
(DE) as the baseline algorithm for proof-of-concept study. Regression models
are trained to model the DE's empirical performance given a parameter
configuration. In particular, we evaluate and compare four popular regression
algorithms both in terms of how well they predict the empirical performance
with respect to a particular parameter configuration, and also how well they
approximate the parameter versus the empirical performance landscapes
Towards the Evolution of Novel Vertical-Axis Wind Turbines
Renewable and sustainable energy is one of the most important challenges
currently facing mankind. Wind has made an increasing contribution to the
world's energy supply mix, but still remains a long way from reaching its full
potential. In this paper, we investigate the use of artificial evolution to
design vertical-axis wind turbine prototypes that are physically instantiated
and evaluated under approximated wind tunnel conditions. An artificial neural
network is used as a surrogate model to assist learning and found to reduce the
number of fabrications required to reach a higher aerodynamic efficiency,
resulting in an important cost reduction. Unlike in other approaches, such as
computational fluid dynamics simulations, no mathematical formulations are used
and no model assumptions are made.Comment: 14 pages, 11 figure
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning
Dynamic environments pose great challenges for expensive optimization
problems, as the objective functions of these problems change over time and
thus require remarkable computational resources to track the optimal solutions.
Although data-driven evolutionary optimization and Bayesian optimization (BO)
approaches have shown promise in solving expensive optimization problems in
static environments, the attempts to develop such approaches in dynamic
environments remain rarely unexplored. In this paper, we propose a simple yet
effective meta-learning-based optimization framework for solving expensive
dynamic optimization problems. This framework is flexible, allowing any
off-the-shelf continuously differentiable surrogate model to be used in a
plug-in manner, either in data-driven evolutionary optimization or BO
approaches. In particular, the framework consists of two unique components: 1)
the meta-learning component, in which a gradient-based meta-learning approach
is adopted to learn experience (effective model parameters) across different
dynamics along the optimization process. 2) the adaptation component, where the
learned experience (model parameters) is used as the initial parameters for
fast adaptation in the dynamic environment based on few shot samples. By doing
so, the optimization process is able to quickly initiate the search in a new
environment within a strictly restricted computational budget. Experiments
demonstrate the effectiveness of the proposed algorithm framework compared to
several state-of-the-art algorithms on common benchmark test problems under
different dynamic characteristics
An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization
Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the increased number of decision variables in EMOPs, the prediction accuracy of surrogate models will deteriorate, which inevitably worsens the performance of SAEAs. To deal with this issue, this article suggests an ensemble surrogate-based framework for tackling EMOPs. In this framework, a global surrogate model is trained under the entire search space to explore the global area, while a number of surrogate submodels are trained under different search subspaces to exploit the subarea, so as to enhance the prediction accuracy and reliability. Moreover, a new infill sampling criterion is designed based on a set of reference vectors to select promising samples for training the models. To validate the generality and effectiveness of our framework, three state-of-the-art evolutionary algorithms [nondominated sorting genetic algorithm III (NSGA-III), multiobjective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE) and reference vector-guided evolutionary algorithm (RVEA)] are embedded, which significantly improve their performance for solving most of the test EMOPs adopted in this article. When compared to some competitive SAEAs for solving EMOPs with up to 30 decision variables, the experimental results also validate the advantages of our approach in most cases
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
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