Many-objective optimization of wireless sensor network deployment

Amor, Omar Ben; Dagdia, Zaineb Chelly; Bechikh, Slim; Saïd, Lamjed Ben

doi:10.1007/s12065-022-00784-1

Cited by 4 publications

(3 citation statements)

References 47 publications

(72 reference statements)

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“…Research in evolutionary computation has primarily focused on problems having two or three objectives Coello et al (2007). However, many real-world problems have several (more than three) objectives in their formulation, e.g., car sideimpact Deb et al (2009), mechanical engineering problems Ursem and Justesen (2012), water resource management Asafuddoula et al (2015), routing planning in agricultural mobile robots Zhang et al (2022), wireless sensor network deployment Ben Amor et al (2022), among others. Particularly, dnDD has intrinsically various objectives to optimise, clearly more than three.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective and Many-Objective Optimisation: Present and Future in de novo Drug Design

S. Angelo,

Alvim Guedes,

J. C. Barbosa

et al. 2023

Preprint

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de novo Drug Design (dnDD) is a multi-objective optimisation problem (MultiOOP) that aims to design new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimisation process, dnDD is naturally categorised as a many-objective optimisation problem (ManyOOP), where more than three objectives must be simultaneously optimised. However, many objectives typically pose several challenges that affect the choice and the design of optimisation methodologies. Herein, we cover the application of MultiOO and ManyOO methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyse how molecular properties used in the optimisation process are applied as either objectives or constraints to the problem. Finally, we discuss future research in ManyOO 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 within the ManyOO field.

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Section: Introductionmentioning

confidence: 99%

Multi-Objective and Many-Objective Optimisation: Present and Future in de novo Drug Design

S. Angelo,

Alvim Guedes,

J. C. Barbosa

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, many real-world problems have several (more than three) objectives in their formulation, e.g. , car side-impact Deb et al (2009) , mechanical engineering problems Ursem and Justesen (2012) , water resource management Asafuddoula et al (2015) , routing planning in agricultural mobile robots Zhang et al (2022) , wireless sensor network deployment Ben Amor et al (2022) , among others. Particularly, dnDD has intrinsically various objectives to optimize, clearly more than three Rosenthal and Borschbach (2017) ; Lambrinidis and Tsantili-Kakoulidou (2021) .…”

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

“…Research in evolutionary computation has primarily focused on problems having two or three objectives Coello et al (2007). However, many real-world problems have several (more than three) objectives in their formulation, e.g., car side-impact Deb et al (2009), mechanical engineering problems Ursem and Justesen (2012), water resource management Asafuddoula et al (2015), routing planning in agricultural mobile robots Zhang et al (2022), wireless sensor network deployment Ben Amor et al (2022), among others. Particularly, dnDD has intrinsically various objectives to optimise, clearly more than three.…”

Section: Introductionmentioning

confidence: 99%

Multi- and Many-Objective Optimisation: Present and Future in de novo Drug Design

S. Angelo,

A. Guedes,

J. C. Barbosa

et al. 2023

Preprint

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 optimisation process, dnDD is naturally categorised as a many-objective optimisation problem (ManyOOP), where more than three objectives must be simultaneously optimised. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimisation methodologies. Herein, we cover the application of multi- and many-objective optimisation methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyse how molecular properties used in the optimisation process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimisation 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

Many-objective optimization of wireless sensor network deployment

Cited by 4 publications

References 47 publications

Multi-Objective and Many-Objective Optimisation: Present and Future in de novo Drug Design

Multi-Objective and Many-Objective Optimisation: Present and Future in de novo Drug Design

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

Multi- and Many-Objective Optimisation: Present and Future in de novo Drug Design

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