Guiding surrogate-assisted multi-objective optimisation with decision maker preferences

Gibson, Finley J.; Everson, Richard; Fieldsend, Jonathan E.

doi:10.1145/3512290.3528814

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…A few problems could be transformed into SO problems because the amount of priority of objectives has been determined by a designer's desire or their conditions. A priori preference specification refers to such approaches, which include weighted min-max [33], weighted sum [3], goal programming [34], and lexicographic approaches [35].…”

Section: Multi-objective Optimization Techniquesmentioning

confidence: 99%

“…There are several challenges with solving optimization problems, the properties of optimization problems vary and are not all the same. Uncertainty, dynamicity, multiple objectives, constraints, and various objectives are a few of such challenges or characteristics [3] [4]. The position of optimum global shifts with time in dynamic problems.…”

Section: Introductionmentioning

confidence: 99%

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Analytical and Comparative Study for Optimization Problems

2023

IJCCCE

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Finding an optimal solution to some problem, like minimizing and maximizing the objective function, is the goal of Single-Objective Optimization (SOP). Real-world problems, on the other hand, are more complicated and involve a wider range of objectives, several objectives should be maximized in such problems. No single solution could be enhanced in all objectives without deteriorating at least one other goal, which is the definition of Pareto-optimality. Understanding the idea of Multi-Objective Optimization (MOP) is thus necessary to find the optimum solution. Multi-objective evolutionary algorithm (MOEA) are made to simultaneously assess many objectives and find Pareto-optimal solutions, MOEA can resolve multi-objective and single-objective optimization problems. This paper aims to introduce a survey study for optimization problem solutions by comparing techniques, advantages, and disadvantages of SOP and MOP with metaheuristics and evolutionary algorithms. From this study, we conduct that the efficiency of MOP lies in the present more than one SOP, but it takes a longer time to process and train and is not suitable for all applications, While SOP is faster and more useful in stock and profit maximization applications. And the posterior techniques are considered the dominant approach to solving multi-objective problems by the use of the field of metaheuristics. Index Terms— Multi-Objective Evolutionary Algorithm, Multi-Objective Optimization, Optimization problem, Objective Function, Single-Objective Optimization.

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Section: Multi-objective Optimization Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analytical and Comparative Study for Optimization Problems

2023

IJCCCE

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Transferable preference learning in multi-objective decision analysis and its application to hydrocracking

Yu,

Wang,

Jiang

et al. 2024

Complex Intell. Syst.

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Hydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low boiling points. It plays a pivotal role in enhancing the quality of products within the oil refining process. Consequently, the development of efficient surrogate models for simulating the hydrocracking process and identifying appropriate solutions for multi-objective oil refining is now an important area of research. In this study, a novel transferable preference learning-driven evolutionary algorithm is proposed to facilitate multi-objective decision analysis in the oil refining process. Specifically, our approach involves considering user preferences to divide the objective space into a region of interest (ROI) and other subspaces. We then utilize Kriging models to approximate the sub-problems within the ROI. In order to enhance the robustness and generalization capability of the Kriging models during the evolutionary process, we transfer the mutual information between the sub-problems in the ROI. To validate the effectiveness as well as efficiency of our proposed method, we undertake a series of experiments on both benchmarks and the oil refining process. The experimental results conclusively demonstrate the superiority of our approach.

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