Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

Satō, Hiroyuki; Ishibuchi, Hisao

doi:10.1002/tee.23796

Cited by 4 publications

(1 citation statement)

References 128 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the past three decades, many-objective EAs (ManyOEAs) have been the subject of extensive research and practical implementation in various real-world applications, making them a widely studied and applied field Safi et al (2018) ; Sato and Ishibuchi (2023) .…”

Section: Introductionmentioning

confidence: 99%

Multi-and many-objective optimization: present and future in de novo drug design

Angelo,

Guedes,

Barbosa

et al. 2023

Front. Chem.

View full text Add to dashboard Cite

de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-and many-objective optimization: present and future in de novo drug design

Angelo,

Guedes,

Barbosa

et al. 2023

Front. Chem.

View full text Add to dashboard Cite

show abstract

A Multi‐Objective Evolutionary Algorithm Based on Bilayered Decomposition for Constrained Multi‐Objective Optimization

Yasuda,

Kumagai,

Tamura

et al. 2024

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

This paper proposes a multi‐objective evolutionary algorithm based on bilayered decomposition (MOEA/BLD) for solving constrained multi‐objective optimization problems. MOEA/D is an effective method for solving unconstrained multi‐objective optimization problems. It decomposes the objective space using weight vectors and simultaneously searches for solutions for the subproblems. However, real‐world applications impose many constraints, and these constraints must be handled appropriately when searching for good feasible solutions. The proposed MOEA/BLD treats such constraints as an additional objective function. Furthermore, in addition to the conventional weight vector, an augmented weight vector is introduced that decomposes the objective space and constraint violation space hierarchically. In the first stage, the objective space is decomposed by conventional weight vectors. In the next stage, the bi‐objective space consisting of the scalarizing function and constraint violation is decomposed by augmented weight vectors. The augmented weights are adjusted so that they decrease linearly in the search process as the search gradually moves from infeasible regions to feasible regions. The proposed algorithm is compared to several state‐of‐the‐art constrained MOEA/Ds using multi‐ and many‐objective problems. The results show that the proposed method outperforms existing methods, in terms of search performance, under various conditions. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

show abstract

Should Multi-objective Evolutionary Algorithms Use Always Best Non-dominated Solutions as Parents?

Sato,

Okumura,

Takadama

et al. 2024

2024 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

Cited by 4 publications

References 128 publications

Multi-and many-objective optimization: present and future in de novo drug design

Multi-and many-objective optimization: present and future in de novo drug design

A Multi‐Objective Evolutionary Algorithm Based on Bilayered Decomposition for Constrained Multi‐Objective Optimization

Should Multi-objective Evolutionary Algorithms Use Always Best Non-dominated Solutions as Parents?

Contact Info

Product

Resources

About