Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system

IEEE Trans. Evol. Computat.

Wang

Chugh

et al. 2019

Self Cite

465

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: E On-line Small Data-driven Optimization Of An Air Intake Vmentioning

confidence: 99%

Data-Driven Evolutionary Optimization: An Overview and Case Studies

IEEE Trans. Evol. Computat.

Wang

Chugh

et al. 2019

Self Cite

465

144

“…For instances, Singh et al proposed a surrogate-assisted simulated annealing algorithm (SASA) for constrained multi-objective optimization [77], Ahmed and Qin proposed a non-dominated sorting based SAEA for multi-objective aerothermodynamic design [78]. Recently, Chugh et al proposed a reference vector-guided surrogateassisted evolutionary algorithm for solving expensive MOPs with more than three objectives [79,80], which was also applied to design the air intake ventilation system [81].…”

Section: Multi-objective Saeasmentioning

confidence: 99%

Model-based evolutionary algorithms: a short survey

Cheng

et al. 2018

Complex Intell. Syst.

The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given.

“…I N solving real-world optimization problems, computationally intensive numerical simulations or physical experiments often need to be conducted to evaluate the objective functions, e.g., in integrated circuit design [1], antenna design [2], hybrid vehicle control [3], or aerodynamic design optimization [4], [5]. In many other situations, only historical data are available for optimization [6]- [10], where the optimization problem is solved on the basis of collected data without resorting to any physical models.…”

Section: Introductionmentioning

confidence: 99%

Off-line Data-driven Multi-objective Optimization: Knowledge Transfer between Surrogates and Generation of Final Solutions

Yang

Ding

IEEE Trans. Evol. Computat.

et al. 2020

In off-line data-driven optimization, only historical data is available for optimization, making it impossible to validate the obtained solutions during the optimization. To address these difficulties, this paper proposes an evolutionary algorithm assisted by two surrogates, one coarse model and one fine model. The coarse surrogate aims to guide the algorithm to quickly find a promising sub-region in the search space, whereas the fine one focuses on leveraging good solutions according to the knowledge transferred from the coarse surrogate. Since the obtained Pareto optimal solutions have not been validated using the real fitness function, a technique for generating the final optimal solutions is suggested. All achieved solutions during the whole optimization process are grouped into a number of clusters according to a set of reference vectors. Then, the solutions in each cluster are averaged and outputted as the final solution of that cluster. The proposed algorithm is compared with its three variants and two state-of-the-art off-line datadriven multi-objective algorithms on eight benchmark problems to demonstrate its effectiveness. Finally, the proposed algorithm is successfully applied to an operational indices optimization problem in beneficiation processes.Index Terms-Off-line data-driven optimization; multisurrogate; knowledge transfer; multi-objective evolutionary algorithms.