6,873 research outputs found
Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation
tools for computationally expensive problems (CEPs). However, a randomly
selected algorithm may fail in solving unknown problems due to no free lunch
theorems, and it will cause more computational resource if we re-run the
algorithm or try other algorithms to get a much solution, which is more serious
in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce
the risk of choosing an inappropriate algorithm for CEPs. We propose two
portfolio frameworks for very expensive problems in which the maximal number of
fitness evaluations is only 5 times of the problem's dimension. One framework
named Par-IBSAEA runs all algorithm candidates in parallel and a more
sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound
(UCB) policy from reinforcement learning to help select the most appropriate
algorithm at each iteration. An effective reward definition is proposed for the
UCB policy. We consider three state-of-the-art individual-based SAEAs on
different problems and compare them to the portfolios built from their
instances on several benchmark problems given limited computation budgets. Our
experimental studies demonstrate that our proposed portfolio frameworks
significantly outperform any single algorithm on the set of benchmark problems
Scalarizing Functions in Bayesian Multiobjective Optimization
Scalarizing functions have been widely used to convert a multiobjective
optimization problem into a single objective optimization problem. However,
their use in solving (computationally) expensive multi- and many-objective
optimization problems in Bayesian multiobjective optimization is scarce.
Scalarizing functions can play a crucial role on the quality and number of
evaluations required when doing the optimization. In this article, we study and
review 15 different scalarizing functions in the framework of Bayesian
multiobjective optimization and build Gaussian process models (as surrogates,
metamodels or emulators) on them. We use expected improvement as infill
criterion (or acquisition function) to update the models. In particular, we
compare different scalarizing functions and analyze their performance on
several benchmark problems with different number of objectives to be optimized.
The review and experiments on different functions provide useful insights when
using and selecting a scalarizing function when using a Bayesian multiobjective
optimization method
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
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
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy
This paper presents a novel mechanism to adapt surrogate-assisted
population-based algorithms. This mechanism is applied to ACM-ES, a recently
proposed surrogate-assisted variant of CMA-ES. The resulting algorithm,
saACM-ES, adjusts online the lifelength of the current surrogate model (the
number of CMA-ES generations before learning a new surrogate) and the surrogate
hyper-parameters. Both heuristics significantly improve the quality of the
surrogate model, yielding a significant speed-up of saACM-ES compared to the
ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the
BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability
w.r.t the problem dimension and the population size of the proposed approach,
that reaches new best results on some of the benchmark problems.Comment: Genetic and Evolutionary Computation Conference (GECCO 2012) (2012
Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models
It has been shown that cooperative coevolution (CC) can effectively deal with
large scale optimization problems (LSOPs) through a divide-and-conquer
strategy. However, its performance is severely restricted by the current
context-vector-based sub-solution evaluation method since this method needs to
access the original high dimensional simulation model when evaluating each
sub-solution and thus requires many computation resources. To alleviate this
issue, this study proposes an adaptive surrogate model assisted CC framework.
This framework adaptively constructs surrogate models for different
sub-problems by fully considering their characteristics. For the single
dimensional sub-problems obtained through decomposition, accurate enough
surrogate models can be obtained and used to find out the optimal solutions of
the corresponding sub-problems directly. As for the nonseparable sub-problems,
the surrogate models are employed to evaluate the corresponding sub-solutions,
and the original simulation model is only adopted to reevaluate some good
sub-solutions selected by surrogate models. By these means, the computation
cost could be greatly reduced without significantly sacrificing evaluation
quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the
concrete algorithm based on this framework is able to find much better
solutions than the conventional CC algorithms and a non-CC algorithm even with
much fewer computation resources.Comment: arXiv admin note: text overlap with arXiv:1802.0974
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