2,499 research outputs found
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation-based algorithms. We also compare these algorithms based on different criteria such as metamodeling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive multiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available.The research of Tinkle Chugh was funded by the COMAS Doctoral Program (at the University of Jyväskylä) and FiDiPro Project DeCoMo (funded by Tekes, the Finnish Funding Agency for Innovation), and the research of Dr. Karthik Sindhya was funded by SIMPRO project funded by Tekes as well as DeCoMo
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
Evolutionary model type selection for global surrogate modeling
Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type
Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks
In the paper, a multi-objective evolutionary surrogate-assisted approach for
the fast and effective generative design of coastal breakwaters is proposed. To
approximate the computationally expensive objective functions, the deep
convolutional neural network is used as a surrogate model. This model allows
optimizing a configuration of breakwaters with a different number of structures
and segments. In addition to the surrogate, an assistant model was developed to
estimate the confidence of predictions. The proposed approach was tested on the
synthetic water area, the SWAN model was used to calculate the wave heights.
The experimental results confirm that the proposed approach allows obtaining
more effective (less expensive with better protective properties) solutions
than non-surrogate approaches for the same time
Multi-Fidelity Methods for Optimization: A Survey
Real-world black-box optimization often involves time-consuming or costly
experiments and simulations. Multi-fidelity optimization (MFO) stands out as a
cost-effective strategy that balances high-fidelity accuracy with computational
efficiency through a hierarchical fidelity approach. This survey presents a
systematic exploration of MFO, underpinned by a novel text mining framework
based on a pre-trained language model. We delve deep into the foundational
principles and methodologies of MFO, focusing on three core components --
multi-fidelity surrogate models, fidelity management strategies, and
optimization techniques. Additionally, this survey highlights the diverse
applications of MFO across several key domains, including machine learning,
engineering design optimization, and scientific discovery, showcasing the
adaptability and effectiveness of MFO in tackling complex computational
challenges. Furthermore, we also envision several emerging challenges and
prospects in the MFO landscape, spanning scalability, the composition of lower
fidelities, and the integration of human-in-the-loop approaches at the
algorithmic level. We also address critical issues related to benchmarking and
the advancement of open science within the MFO community. Overall, this survey
aims to catalyze further research and foster collaborations in MFO, setting the
stage for future innovations and breakthroughs in the field.Comment: 47 pages, 9 figure
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