Selection mechanisms based on the maximin fitness function to solve multi-objective optimization problems

Menchaca-Méndez, Adriana; Coello, Carlos A. Coello

doi:10.1016/j.ins.2015.11.008

Cited by 17 publications

(8 citation statements)

References 13 publications

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“…The performance can be improved by inserting sensitivity, robustness objectives and constraints (Casavola et al, 2014;Gutiérrez-Carvajal et al, 2016). The pole placement method applied in this paper can be replaced by the optimal design and tuning by means of classical or modern optimization algorithms (Bandarabadi et al, 2015;Johanyák, 2015;Menchaca-Mendez and Coello Coello, 2016). This has not been investigated, but it represents a subject of future research.…”

Section: Discussionmentioning

confidence: 99%

State Feedback Control Solutions for a Mechatronics System with Variable Moment of Inertia

Szedlak-Stînean

Precup

Preitl

et al. 2016

Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics

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This paper presents details regarding the design of two state feedback control (SFC) solutions for the position control of a mechatronics application represented by the Model 220 Industrial Plant Emulator. Since SFC is not effective in terms of zero steady-state control error, the SFC structure of both solutions is inserted in a control loop that contains a PID controller with or without low-pass filter. This leads to the two SFC solutions proposed in this paper and dedicated to mechatronics applications with variable moment of inertia. The PID controllers are tuned by the Modulus Optimum method to ensure high control system performance expressed as increased phase margins and improved tracking performance. The performance of the proposed SFC solutions is illustrated by case studies that deal with experimentally identified parameters in two situations, rigid body dynamics and flexible drive dynamics. Simulation and experimental results obtained for the three significant values of the moment of inertia of the load disk are given.

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Section: Discussionmentioning

confidence: 99%

State Feedback Control Solutions for a Mechatronics System with Variable Moment of Inertia

Szedlak-Stînean

Precup

Preitl

et al. 2016

Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics

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“…It is necessary to introduce the concept of Pareto Dominance to determine the solution of a multi-objective optimization problem. Menchaca-Mendez & Coello (2016) Finding the Pareto front is an important step in a multi-objective optimization, however it does not stablish a complete order for the solutions of the problem. Hence, there are three different approaches to find a final solution: a priori, interactive and a posteriori.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

A hybrid multi-objective genetic algorithm for scheduling heterogeneous workover rigs on onshore oil fields

Tozzo

Costa

Lins

2020

Journal of Petroleum Science and Engineering

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Agradeço aos meus pais, Aparecido e Elza, por estarem sempre presentes na minha vida, independentemente da distância, e por serem minha inspiração na busca por novos desafios. Agradeço por confiarem em minhas decisões, pelo apoio incondicional e incentivo constante para que meus sonhos sejam realizados. À minha irmã e meu cunhado, Ana Paula e Francis, pelo apoio e encorajamento durante as diferentes fases de desenvolvimento deste trabalho. Em especial, agradeço à Francis por compartilhar sua experiência e contribuir com dicas importantes durante a fase de implementação do meu algoritmo. Aos meus companheiros de mestrado, Caio, Gisele, Patrícia e Thaís, pela convivência e apoio mútuo, seja durante o desenvolvimento das atividades acadêmicas, seja durante nossos momentos de descontração. Tenho um carinho especial por cada um de vocês. Aos amigos com quem tive a oportunidade de dividir apartamento, Alan, Cesar, Flávia, Lenon, Marcelo e Rafaela, por me acolherem em Recife e tornarem esse processo de adaptação o menos tumultuado possível. Vocês se mostraram uma verdadeira família para mim. À minha orientadora Ana Paula Cabral, pela confiança depositada, pela transmissão de conhecimentos e experiências durante a elaboração deste trabalho. À professora Isis Lins, por aceitar o convite para participação dessa dissertação e fornecer contribuições valiosas para o aperfeiçoamento do trabalho. Ao Programa de Pós-Graduação em Engenharia de Produção da UFPE (PPGEP), pela oportunidade de estudo, pelos conhecimentos transmitidos e estrutura disponibilizada. Ao CNPq, pelo apoio financeiro durante o desenvolvimento desta pesquisa. De modo geral, agradeço a todos que acreditaram em mim e que contribuíram, de alguma forma, para que essa dissertação pudesse ser realizada. A todos sou muito grato.

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“…For indicator-based MOEAs to become more popular, other performance indicators need to be proposed. Although there has been some research activity in this regard (see for example [113,140]) none of these other performance indicators has become as popular as the hypervolume. Another interesting idea is the combination of performance indicators in order to take advantage of their strengths and compensate for their limitations (see for example [48]).…”

Section: Challengesmentioning

confidence: 99%

Evolutionary multiobjective optimization: open research areas and some challenges lying ahead

Coello¹,

Brambila²,

Gamboa³

et al. 2019

Complex Intell. Syst.

126

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Evolutionary multiobjective optimization has been a research area since the mid-1980s, and has experienced a very significant activity in the last 20 years. However, and in spite of the maturity of this field, there are still several important challenges lying ahead. This paper provides a short description of some of them, with a particular focus on open research areas, rather than on specific research topics or problems. The main aim of this paper is to motivate researchers and students to develop research in these areas, as this will contribute to maintaining this discipline active during the next few years.

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Selection mechanisms based on the maximin fitness function to solve multi-objective optimization problems

Cited by 17 publications

References 13 publications

State Feedback Control Solutions for a Mechatronics System with Variable Moment of Inertia

State Feedback Control Solutions for a Mechatronics System with Variable Moment of Inertia

A hybrid multi-objective genetic algorithm for scheduling heterogeneous workover rigs on onshore oil fields

Evolutionary multiobjective optimization: open research areas and some challenges lying ahead

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