Open research questions in Advanced Multi-Objective Optimization Algorithms
139 gap statements mined from Advanced Multi-Objective Optimization Algorithms papers in our 4.5M-paper local library, which holds 1,846 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.
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What the literature leaves open
It notes that traditional optimization methods are often insufficient for this task due to uncertainties in material properties, dimensions, and external loads. The two-layer computational structure of RBDO is also identified as a challenge.
The need for a method to design regular microreliefs that satisfy surface-coverage, lubricant-retention, and residual-depth requirements. The lack of a comprehensive approach to formulate the task as a two-objective inverse problem for rigid-ball burnishing of external cylinders and compliant-roller burnishing of internal tubes.
Two-Phase Multi-Objective Inverse Design of Rotary Burnishing Regimes and Compliant-Roller Tools: Full-Factorial Grid Enumeration and NSGA-II · 2026 · DOILack of standardized problem formulation. Inadequacy of traditional design approaches for modern engineering systems. Need for a comprehensive framework for AI-driven design problems.
AI-driven design: a comprehensive review of its methodologies, applications, and challenges · 2026 · DOIThe paper identifies the challenge of optimizing expensive black-box problems with high computational costs and a lack of analytical expressions. It notes the difficulty of avoiding redundant sampling in already-exploited areas. The paper also highlights the importance of balancing global exploration and focused local search in optimization.
Data-driven multi-phase optimization for expensive black-box problems via enhanced niche identification and tabu subspace filtering · 2026 · DOIThe paper identifies a gap in existing optimization methods for expensive black-box problems, which can be time-consuming and inefficient. The gap is particularly significant in high-precision fields where simulation models are computationally expensive. The paper seeks to address this gap with a novel optimization approach.
Data-driven multi-phase optimization for expensive black-box problems via enhanced niche identification and tabu subspace filtering · 2026 · DOITraditional methods for exploring design spaces are less practical due to the high computational cost and inherent complexity of the data structures involved. There is a need for a unified framework to address the challenges of hierarchical spaces. The existing approaches do not effectively capture inter-variable hierarchical relationships.
Modeling hierarchical spaces: a review and unified framework for surrogate-based architecture design · 2026 · DOIThe lack of uncertainty quantification in traditional data-based optimization methods. The risk of optimization being misled by model errors. The need for more robust optimization frameworks in aerodynamic design.
The multiple iterations required to solve the MDA can represent an important computational burden. The high computational cost of solving the MDO problem. The need to balance the accuracy of the MDA solution and the computational cost.
Prior multi-fidelity surrogate modeling methods have limitations, such as division-by-zero errors or instability. There is a need for a more accurate and robust multi-fidelity surrogate modeling framework.
A multi-fidelity surrogate modeling framework based on multivariate adaptive regression splines · 2026 · DOISeveral active learning schemes have been proposed to accelerate identification of the composite feasible region that satisfies all constraints. However, these methods have limitations, such as requiring integration across the entire design space or only using information at the candidate sample point.
Active learning of constraint boundaries using expected magnitude of incorrectness and neural networks · 2026 · DOIThe complexity of the beam structure of bus frames has limited prior work to optimizing specific local regions. There is a need for a multi-level structural optimization design that considers the overall beam structure. Prior work has not fully addressed the problem of reducing weight while meeting strength requirements.
The traditional single-fidelity surrogates and competitive multi-model fusion surrogates have limitations in terms of approximation accuracy and robustness. The existing multi-model fusion surrogates do not consider the promising parameter domain. The lack of an efficient method to calculate the weight coefficient for each basis multi-model fusion surrogate.
Multi-model fusion surrogates ensemble using promising parameter domain for flight vehicle system design · 2026 · DOIThe need for a machine learning-driven thermo-mechanical collaborative optimization framework for flexible substrates. The lack of a novel composite substrate that can balance thermal protection and mechanical flexibility.
Machine learning-based optimization design method for thermal protection substrates in flexible wearable devices · 2026 · DOIWhile data-driven dimensionality reduction techniques, such as mode decomposition and deep learning, offer potential solutions, they generally require extensive pre-existing high-quality geometric datasets, which are scarce for 3D configurations, especially unconventional aircraft.
Enhancing surrogate-based aerodynamic shape optimization via dataset-independent generative modelling and dimensionality reduction · 2026 · DOIBy constructing a behaviour space that integrates objective and constraint information, the mechanism emphasizes novelty-driven selection when feasibility is scarce to enhance coverage and suppress clustering, and then gradually shifts toward crowding-driven exploitation once feasibility becomes sufficient to stabilize convergence and control computational overhead.
DPNCMO: A novelty-guided dual-population algorithm with adaptive constraint relaxation for constrained multi-objective optimization · 2026 · DOINevertheless, when feasible regions are sparse or highly fragmented, many existing methods still suffer from slow feasibility attainment, a high proportion of ineffective evaluations, and inadequate front coverage, leading to premature clustering.
DPNCMO: A novelty-guided dual-population algorithm with adaptive constraint relaxation for constrained multi-objective optimization · 2026 · DOIExisting methods consider only limited classes of dynamic events. There is room for performance improvement in dynamic task allocation.
Obtaining large volumes of high-fidelity data remains challenging in practical applications. The curse of dimensionality leads to rapidly increasing computational costs and model overfitting. The VAE-based dimensionality reduction may not capture all the important features of the design space.
Augmenting purely data-driven confidence estimation with physics-embedded measures, such as uncertainty indicators derived from flow-field residuals or physical consistency…
Limited training data for aerodynamic optimization - Strong nonlinearity in aerodynamic behavior - The curse of dimensionality in prior methods such…
Comparative clustering experiments can be designed with different parameter combinations, - The impact of the eight newly added derivative parameters on cluster analysis can be examined, - The study of the structural optimization of ship side subjected to external collision can be further…
Samples mining and optimization of simplified ship collision problem that based on unsupervised learning with surrogate models and information efficiency methodologies · 2026 · DOIExtending the proposed approach to other MDO formulations, such as Individual Discipline Feasible (IDF), - Applying the proposed approach to more complex engineering problems, - Investigating the use of other surrogate models, such as neural…
Studying the performance of MFSMs under varying cost ratios. Developing new methods to improve the accuracy and robustness of MFSMs. Evaluating the effectiveness of MFSMs in real-world engineering applications.
A multi-fidelity surrogate modeling framework based on multivariate adaptive regression splines · 2026 · DOIFurther research on the application of the proposed framework to other AUV shapes - Investigation of other turbulence models for AUV shape optimization - Extension of the framework to consider other factors that influence the design of high-endurance…
A Bayesian algorithm coupled simulation-driven optimization framework for shape design of autonomous underwater vehicles · 2026 · DOIInvestigating the application of Radial Basis Function surrogate models to other structural design problems, - Comparing the performance of different surrogate modeling techniques in multi-objective optimization, - Exploring the use of other optimization algorithms for flyover design, - Investigating the impact of parameter uncertainty on optimization…
Comparative analysis of multi-objective optimization algorithms for sustainable flyover design framework · 2026 · DOI
Most-cited papers in Advanced Multi-Objective Optimization Algorithms
- Optimization by Simulated Annealing · Science · 1983 · 34,142 citations
- RIME: A physics-based optimization · Neurocomputing · 2023 · 1,068 citations
- A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems · IEEE Access · 2021 · 970 citations
- Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: A critical review · Journal of Materials Research and Technology · 2021 · 472 citations
- An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems · Information Sciences · 2021 · 448 citations
- A Survey on Evolutionary Constrained Multiobjective Optimization · IEEE Transactions on Evolutionary Computation · 2022 · 396 citations
- Deep Reinforcement Learning for Multiobjective Optimization · IEEE Transactions on Cybernetics · 2021 · 387 citations
- A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts · IEEE/CAA Journal of Automatica Sinica · 2021 · 350 citations
- Artificial lemming algorithm: a novel bionic meta-heuristic technique for solving real-world engineering optimization problems · Artificial Intelligence Review · 2025 · 343 citations
- A Review of Multi-objective Optimization: Methods and Algorithms in Mechanical Engineering Problems · Archives of Computational Methods in Engineering · 2021 · 343 citations
Most recent work
- Dual-population hybrid prediction co-evolutionary algorithm for dynamic constrained multiobjective optimization · Expert Systems with Applications · 2026
- Multiknowledge Adaptive Transfer Framework and Auxiliary Value Assessment Strategy for Constrained Multiobjective Optimization · IEEE Transactions on Systems Man and Cybernetics Systems · 2026
- A Coevolutionary Response Framework for Dynamic Constrained Multiobjective Optimization Problems · IEEE Transactions on Evolutionary Computation · 2026
- K-DPSA: A hierarchical surrogate-assisted evolutionary algorithm for expensive constrained multi-objective optimization with application to UAV path planning · Swarm and Evolutionary Computation · 2026
- A Multicriteria Game-Driven Constrained Multiobjective Evolutionary Algorithm · IEEE Transactions on Evolutionary Computation · 2026
- Multi-objective hyper-heuristics: a survey · Artificial Intelligence Review · 2026
- Adaptive small-family population-guided swarm intelligence optimization algorithm · Science China Information Sciences · 2026
- A comprehensive review on deep learning for multi-objective optimization · Swarm and Evolutionary Computation · 2026
- A multitype constraint handling evolutionary algorithm for constrained multiobjective optimization problems · Swarm and Evolutionary Computation · 2026
- An Auxiliary Problem-Assisted Evolutionary Algorithm With Dynamics Regulation for Complex Constrained Multiobjective Optimization · IEEE Transactions on Systems Man and Cybernetics Systems · 2026
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