Aug 2026· ACM Transactions on Evolutionary Learning and Optimization· 0 citations· 100 references
Abstract
Multidisciplinary design optimisation (MDO) is a field of optimisation where problems are partitioned into a set of subproblems, or disciplines, with interactions between them. Multi-objective (MO)-MDO considers cases where multiple objectives exist at the problem or subproblem level. Of particular interest are MO-MDO architectures and methods for a posteriori decision making—in which the aim of the optimiser is to produce an approximation of the efficient set of Pareto optimal solutions. A variety of a posteriori MO-MDO methods have been proposed, many of which draw on concepts and tools from evolutionary computation and machine learning. However, these approaches have arisen across a fragmented set of literatures, and there is no unified guide available to support practitioners or steer the progressive development of new methods. This survey aims to provide such a unified perspective. The issue of characterising multi-objectivity in MO-MDO is discussed and a typology is introduced to identify the different methods in an accessible and straightforward way. The available benchmark problems for MO-MDO are also surveyed. Key issues and potential paths for future research in MO-MDO are identified and discussed.
Results show that integrating local search significantly enhances performance, while a principled method for setting hybrid parameters ensures robustness and reproducibility, highlighting the potential of combining mathematical programming techniques with evolutionary algorithms for high-dimensional many-objective optimization problems.
Regina C. L. C. de Sousa, Dênis E. C. Vargas, Elizabeth F. Wanner et al.· Journal of Heuristics· 0 citations
The Pareto set and Pareto front of a continuous multiobjective optimization problem typically contain infinitely many solutions. Since dealing with infinite sets is impractical, they are commonly approximated by finite reference sets. In this way, the performance of multiobjective optimization algorithms can be assessed using quality indicators. However, the selection of a reference set depends on the intended goal, such as achieving a uniform distribution of solutions along the Pareto set or Pareto front, or optimizing a specific quality indicator. In this paper, we investigate and compare six strategies for reference set construction in constrained bi-objective problems from a recently proposed test problem generator. The approaches are evaluated with respect to multiple quality indicators and computational cost. The experiments are performed on test problems with irregular Pareto sets and fronts. From these experiments, we conclude that the choice of strategy should depend on the intended goal, with the approach that aims to maximize the hypervolume indicator value standing out as the best trade-off in terms of speed and indicator accuracies.
Luka Opravš, D. Brockhoff, T. Tušar· Annual Conference on Genetic...· 0 citations
An objective-wise variable analysis method that first evaluates the sensitivity of each objective to all decision variables, and then comprehensively aggregates the sensitivity information across multiple objectives to estimate the overall importance of decision variables is proposed.
Chuanlong Ye, Fazhi He, Xiaoxin Gao et al.· Journal of King Saud Univers...· 0 citations
This paper compares six different strategies identified in a recent survey, categorizing them as either global strategies (GS), which treat the decision vector as a unified type, or decomposition-based strategies (DS), which split the problem into continuous and discrete subproblems.
Thomas Lang, Denis Pallez· Proceedings of the Genetic a...· 0 citations
This study introduces a novel constrained multi-objective evolutionary algorithm, termed DPCME, which employs two interacting populations that exchange information, enabling effective global exploration and reducing the risk of convergence to local optima.
This work investigates the hybridization of a Genetic Algorithm with Pareto Local Search to improve the exploration of non-dominated rulesets and provides a documented baseline for hybrid metaheuristics in rule mining.
Evgueni Blanquart, L. Jourdan, Nadarajen Veerapen· Proceedings of the Genetic a...· 0 citations