Enhancing the maintenance strategy and cost in systems with surrogate assisted multiobjective evolutionary algorithms

A surrogate-assisted multi-objective evolutionary algorithm with dimension-reduction for production optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have been widely developed to solve complex and computationally expensive multiobjective optimization problems (EMOPs) in recent years. However, when dealing with high-dimensional optimization problems in decision space, the performance of these surrogate-assisted multiobjective evolutionary algorithms (MOEAs) deteriorates drastically. In this work, a novel classifier-assisted rank-based learning and local model-based multiobjective evolutionary algorithm (CLMEA) is proposed for high-dimensional EMOPs. CLMEA makes full use of the uncertainty of solutions in the decision space and objective space to explore the uncertain but informative space toward high-dimensional problems. Specifically, the offspring in different ranks uses rank-based learning strategy to generate more promising and informative candidates for function evaluations (FEs). To reduce the search region of high-dimensional problems and maintain the diversity of solutions, the most uncertain sample point from the nondominated solutions measured by the crowding distance is selected as the center to conduct local search. The experimental results of benchmark problems and a real-world application on geothermal reservoir heat extraction optimization demonstrate superior performance of CLMEA compared with the state-of-the-art surrogate-assisted MOEAs. The source code for this work is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JellyChen7/CLMEA</uri>

Despite the success of Evolutionary Algorithms in solving complex problems, they may require many function evaluations. This becomes an issue when dealing with costly problems. Surrogate models may overcome this difficulty, though their use in problems with medium to large dimensionality is underexplored in the literature. Problems with multiple conflicting objectives can be formulated as Multi-objective Optimization Problems (MOPs). MOPs have received a great attention lately, mainly with Multi-objective Evolutionary Algorithms (MOEAs). This paper proposes ELMOEA/D-DE, a surrogate-assisted MOEA, for solving expensive MOPs in small evaluation budgets. ELMOEA/D-DE encompasses a state-of-the-art MOEA based on decomposition, Differential Evolution (DE) operators and Extreme Learning Machines. This paper tests three variants of ELMOEA/D-DE, using different DE operators, for solving five known benchmark MOPs with 10 to 60 decision variables. All variants achieve good results in terms of hyper volume metric and the best variant with operator DE/rand/1/bin is compared with two state-of-the-art approaches (MOEA/D-RBF and a non-surrogate-based MOEA), achieving the best results in all but one problems instances.

Adaptive surrogate-assisted multi-objective evolutionary algorithm using an efficient infill technique

An improved multiobjective differential evolution based on Pareto-adaptive -dominance and orthogonal design

We consider a job shop scheduling problem with uncertain processing times modelled as triangular fuzzy numbers and propose a multiobjective surrogate-assisted evolutionary algorithm to optimise not only the schedule’s fuzzy makespan but also the robustness of schedules with respect to different perturbations in the durations. The surrogate model is defined to avoid evaluating the robustness measure for some individuals and estimate it instead based on the robustness values of neighbouring individuals, where neighbour proximity is evaluated based on the similarity of fuzzy makespan values. The experimental results show that by using fitness estimation, it is possible to reach good fitness levels much faster than if all individuals are evaluated.

Accelerating surrogate assisted evolutionary algorithms for expensive multi-objective optimization via explainable machine learning

High-dimensional expensive optimization by Kriging-assisted multiobjective evolutionary algorithm with dimensionality reduction

Surrogate-assisted multi-objective evolutionary feature selection of generation-based fixed evolution control for time series forecasting with LSTM networks

An Enhanced multi-objective evolutionary optimization algorithm based on decomposition (E-MOEA-D) proposed for optimal placement of Distributed Generation (DG) and Electric Vehicle (EV) Fast Charging Station (FCS) in distribution system. The diversity of the evolutionary algorithm improves the convergence and diverse solution in the process of evolutionary optimization. The proposed algorithm is improved using enhanced diversity algorithm, which yield diverse candidate solutions in population. The optimal placement of DGs and FCS are formulated using three objective functions as i) Active power loss ii) Voltage deviation iii) DG cost. The proposed algorithm is simulated on IEEE-33 bus distribution system. The proposed algorithm is compared with other competitive multi-objective evolutionary algorithms such as decomposition based multi-objective evolutionary algorithm (MOEA-D) and Non-dominated sorting multi-objective evolutionary algorithm (NSGA-II).

As a branch of frequent pattern mining, the task-oriented pattern mining has received increasing attention due to its broad application scenarios. The lexicographic subset tree based algorithm and the multiobjective evolutionary algorithm are two effective approaches for finding the most frequent and complete pattern in task-oriented applications. However, both suffer from heavy computational cost since their runtime increases rapidly as the transaction dataset is scaled up. To address this issue, this paper regards the task-oriented pattern mining as a data-driven optimization problem and solves it by using a surrogate-assisted multiobjective evolutionary algorithm. Based on the framework of our previous multiobjective evolutionary algorithm for task-oriented pattern mining, the proposed algorithm estimates the objective values of most solutions using an ensemble of surrogates instead of the real objective functions, thereby highly improving the efficiency of the algorithm. Experimental results on three task-oriented applications indicate that the proposed algorithm has better efficiency than state-of-the-art algorithms.

A hyperparameter-optimized Kriging surrogate model was developed for the structural collapse behavior framework presented in this paper. The assessment is conducted on a stiffened panel subject to axial load and lateral pressure, typical of the deck structure of a bulk carrier ship. This behavior is characterized using nonlinear finite element analysis to determine the collapse response. The surrogate model’s hyperparameters were optimized using a Genetic Algorithm to achieve the best performance, and the trained framework can predict ultimate strength. By following this approach, the problem can be reformulated as a multi-objective optimization task. This framework involves associating the Kriging surrogate model with a multi-objective evolutionary optimization algorithm based on Genetic Algorithms to balance the trade-off between the weight and ultimate strength of the stiffened panel. The results confirm the applicability of the Kriging surrogate framework to predict the ultimate strength and assess the collapse analysis of the stiffened panels, ensuring accuracy through GA-based hyperparameter optimization.

In this paper we propose a methodology based on an evolutionary multiobjective optimization algorithm for the problem of up-grading (expansion) the physical topology of transparent optical networks. The aim is to define the new topology layout and the specification of the new optical devices that should be deployed in an existing network in order to improve its performance, considering simultaneously the total installation cost (capital cost) and the total network blocking probability (performance criteria). To accomplish that, we propose a capital cost model for the network. We considered the following physical layer impairments: losses in optical devices, amplified spontaneous emission in optical amplifier and homodyne crosstalk in optical cross connect. The multiobjective optimization evolutionary algorithm used in the optimization process is the NSGA-II. Our proposed methodology can solve the expand physical topology design problem taking into account the physical layer impairments and the capital costs simultaneously. We also present a case study to show the effectiveness of our methodology.

• Comprehensive review of recent advancements in BMS integrating DTs, DL and BD. • Identification and classification of key sources of uncertainty across BMS modules and strategies for their management. • Critical analysis of DT and DL-based frameworks for reliability enhancement and informed decision-making. • Discussion of research gaps and opportunities for integrating advanced technologies into bridge maintenance and monitoring. • Emphasis on future directions toward intelligent, adaptive, and uncertainty-aware bridge management systems Bridge infrastructure, as a critical capital asset and an integral component of transportation networks, is increasingly affected by aging, deterioration, and external damage, potentially compromising its safety, performance, and functionality. These challenges, compounded by the growing demand for infrastructure expansion and limited funding resources, underscore the necessity of adopting efficient bridge management systems to prioritize maintenance and remediation strategies. To address these demands, different Bridge Management Systems (BMSs) have been developed to support operators in maintaining safe operations while optimizing budget allocation and maintenance strategies. Despite advancements in this field, most state-of-the-art research lacks a comprehensive overview of the role of Digital Twins (DTs), Deep learning (DL) and Big Data (BD) integration in enhancing BMS, particularly in managing uncertainties across BMS modules. Therefore, this paper provides a dedicated review of recent research in BMSs, with particular emphasis on the integration of DT and DL-enabled frameworks into bridge management practices and organizes key sources of uncertainty and discusses how emerging technologies can address these aspects in BMSs, which has yet to be comprehensively addressed. The review critically explores current methodologies, highlights challenges and identifies opportunities for uncertainty management in BMSs through the integration of advanced technologies and future trends for more reliable bridge management.

Although surrogate-assisted evolutionary algorithms have been widely utilized in the science and engineering fields, efficient management of surrogates is still a challenging task because of the complicated action mechanisms of surrogates. Trying to investigate how surrogates influence the convergence of evolutionary algorithms, this paper performs an analysis of a (1+1) surrogate-assisted evolutionary algorithm with Gaussian mutations, where a linear surrogate model is employed to approximately evaluate the candidate solutions. Numerical results demonstrate that the linear surrogate model greatly improves performance of evolutionary algorithms on a unimodal problem when the standard deviation of Gaussian mutation σ is small. However, when σ is set big, the improving functions of linear surrogate models are not significant. However, for a multi-modal problem, the positive function of the linear surrogate model is not significant, because the employed Gaussian mutation cannot keep a balance between exploration and exploitation.KeywordsCandidate SolutionSphere FunctionGaussian MutationMultimodal ProblemGaussian Random NumberThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.