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This paper presents a new optimization framework in which the structural analyzer (isogeometric analysis–IGA) and data-driven surrogate model (deep neural network–DNN) are sequentially and repeatedly employed as the evaluation function in the ...

• Tri-directional material distribution optimization in FG plates is considered.
• A hybrid IGA-DNN optimization framework is proposed.
• Training data is collected from the optimization process.
• Significantly reduced computation ...

We consider multiobjective optimization problems with at least one computationally expensive constraint function and propose a novel surrogate-assisted evolutionary algorithm that can incorporate preference information given a priori. We employ ...

Surrogate models are built to produce computationally efficient versions of time-complex simulation-based objective functions so as to address expensive optimization. In surrogate-assisted evolutionary computations, the surrogate model evaluates ...

• Empirical analysis of surrogate-assisted and surrogate-driven algorithms design.
• New hybrid successive acquisition process algorithm for parallel scalability.
• New hybrid algorithm relying on the dispersion metric.

Fitness functions of real-world optimization problems often need to be analyzed by expensive experiments or numerical simulations. Integrating these expensive simulations or experiments directly into optimization algorithms would result in ...

• Propose a novel hierarchical surrogate technique.
• Propose an adaptive infill strategy for enhancing the evolutionary search.
• Develop an efficient surrogate-assisted evolutionary algorithm.

Bilevel evolutionary algorithms (BLEAs) are a plausible approach for bilevel optimization. However, these algorithms require many fitness evaluations (FEs) and might become unusable if the fitness evaluations are computationally expensive. ...

Solving real-life data-driven multiobjective optimization problems involves many complicated challenges. These challenges include preprocessing the data, modelling the objective functions, getting a meaningful formulation of the ...

The performance of modern production systems often depends upon automated production planning strategies such as material requirements planning. Parametrizing, evaluating and comparing these strategies by testing them in the real world is often ...

The utilization of surrogate models to approximate complex systems has recently gained increased popularity. Because of their capability to deal with black-box problems and lower computational requirements, surrogates were successfully utilized by ...

Real-world optimization scenarios under uncertainty and no-ise are typically handled with robust optimization techniques, which re-formulate the original optimization problem into a robust counterpart, e.g., by taking an average of the function ...

In this paper, a method called Lipschitz-surrogate Assisted Differential Evolution (LSADE) is described. The method uses two different surrogates: a standard radial basis function one and a specialized one based on a Lipschitz condition. It also ...

Neuroevolution is a machine learning method for evolving neural networks parameters and topology with a high degree of flexibility that makes them applicable to a wide range of architectures. Neuroevolution has been popular in ...

In the past decades, a number of surrogate-assisted evolutionary algorithms (SAEAs) have been developed to solve expensive multiobjective optimization problems (EMOPs). However, most existing SAEAs focus on low-dimensional optimization problems, ...

In the past years, a significant amount of research has been done in optimizing computationally expensive and time-consuming objective functions using various surrogate modeling approaches. Constraints have often been neglected or assumed to be a ...

This paper presents a multi-fidelity RBF (radial basis function) surrogate-based optimization framework (MRSO) for computationally expensive multi-modal optimization problems when multi-fidelity (high-fidelity (HF) and low-fidelity (LF)) models ...

One-shot optimization tasks require to determine the set of solution candidates prior to their evaluation, i.e., without possibility for adaptive sampling. We consider two variants, classic one-shot optimization (where our aim is to find at least ...

Optimization solutions of real-world engineering problems mainly suffer from the large computational cost, the curse of dimensionality, and the multi-disciplinary nature of the involved disciplines. These issues may be intensified by incorporating ...

Like most evolutionary algorithms, particle swarm optimization (PSO) usually requires a large number of fitness evaluations to obtain a sufficiently good solution. This poses an obstacle for applying PSO to computationally expensive problems. This paper ...

Classification is one of the most well-known tasks in supervised learning. A vast number of algorithms for pattern classification have been proposed so far. Among these, support vector machines (SVMs) are one of the most popular approaches, due to the ...