Expected Improvement of Penalty-Based Boundary Intersection for Expensive Multiobjective Optimization

IEEE Trans. Evol. Computat.

Chugh

et al. 2019

435

144

Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.

Section: A Data Collectionmentioning

confidence: 99%

Section: Off-line Small Data-driven Optimization Of Fused Magnesiumentioning

confidence: 99%

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Jin

IEEE Trans. Evol. Computat.

Chugh

et al. 2019

435

144

“…Also, some research measure the uncertainty based on the variance of surrogate outputs [39], [50]. In addition, a branch of strategies called infill criteria is researched to consider both the prediction fitness and uncertainty together to combine their advantages, including expected lower confidence bound (LCB) [40], probability of improvement (PoI) [51], and expected improvement (ExI) [52]. Furthermore, based on this, multiobjective infill criteria have also shown effectiveness when minimizing fitness and uncertainty together [21].…”

Section: B Related Workmentioning

confidence: 99%

Data-Driven Evolutionary Algorithm With Perturbation-Based Ensemble Surrogates

Zhan

et al. 2021

IEEE Trans. Cybern.

Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive optimization, which is useful and efficient when the objective function of the optimization problem is expensive or difficult to access. However, the performance of DDEAs relies on their surrogate quality and often deteriorates if the amount of available data decreases. To solve these problems, this article proposes a new DDEA framework with perturbation-based ensemble surrogates (DDEA-PES), which contain two efficient mechanisms. The first is a diverse surrogate generation method that can generate diverse surrogates through performing data perturbations on the available data. The second is a selective ensemble method that selects some of the prebuilt surrogates to form a final ensemble surrogate model. By combining these two mechanisms, the proposed DDEA-PES framework has three advantages, including larger data quantity, better data utilization, and higher surrogate accuracy. To validate the effectiveness of the proposed framework, this article provides both theoretical and experimental analyses. For the experimental comparisons, a specific DDEA-PES algorithm is developed as an instance by adopting a genetic algorithm as the optimizer and radial basis function neural networks as the base models. The experimental results on widely used benchmarks and an aerodynamic airfoil design real-world optimization problem show that the proposed DDEA-PES algorithm outperforms some state-of-the-art DDEAs. Moreover, when compared with traditional nondata-driven methods, the proposed DDEA-PES algorithm only requires about 2% computational budgets to produce competitive results.

“…Except for the Kriging models, some researches employ the variance of surrogate outputs to measure the uncertainty [18], [58]. In addition, as evaluating promising and uncertain individuals have different advantages, many strategies called infill criteria are proposed and studied based on the combinations of them, such as expected lower confidence bound [19], probability of improvement [59], and expected improvement [52], [60]. Moreover, Tian et al [13] proposed a multiobjective infill criterion driven GP-assisted social learning PSO (MGP-SLPSO), where the multiobjective infill criteria are shown to be efficient when optimizing fitness and minimizing uncertainty together in solving high dimensional problems.…”

Section: B Related Workmentioning

confidence: 99%

Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation

Zhan

IEEE Trans. Evol. Computat.

et al. 2020

119

By efficiently building and exploiting surrogates, data-driven evolutionary algorithms (DDEAs) can be very helpful in solving expensive and computationally intensive problems. However, they still often suffer from two difficulties. First, many existing methods for building a single ad hoc surrogate are suitable for some special problems but may not work well on some other problems. Second, the optimization accuracy of DDEAs deteriorates if available data are not enough for building accurate surrogates, which is common in expensive optimization problems. To this end, this article proposes a novel DDEA with two efficient components. First, a boosting strategy (BS) is proposed for self-aware model managements, which can iteratively build and combine surrogates to obtain suitable surrogate models for different problems. Second, a localized data generation (LDG) method is proposed to generate synthetic data to alleviate data shortage and increase data quantity, which is achieved by approximating fitness through data positions. By integrating the BS and the LDG, the BDDEA-LDG algorithm is able to improve model accuracy and data quantity at the same time automatically according to the problems at hand. Besides, a tradeoff is empirically considered to strike a better balance between the effectiveness of surrogates and the time cost for building them. The experimental results show that the proposed BDDEA-LDG algorithm can generally outperform both traditional methods without surrogates and other state-of-the-art DDEA son widely used benchmarks and an arterial traffic signal timing real-world optimization problem. Furthermore, the proposed BDDEA-LDG algorithm can use only about 2% computational budgets of traditional methods for producing competitive results.