Physical programming for preference driven evolutionary multi-objective optimization

Preference articulation in multi-objective optimization could be used to improve the pertinency of solutions in an approximated Pareto front. That is, computing the most interesting solutions from the designer's point of view in order to facilitate the Pareto front analysis and the selection of a design alternative. This articulation can be achieved in an a priori, progressive, or a posteriori manner. If it is used within an a priori frame, it could focus the optimization process toward the most promising areas of the Pareto front, saving computational resources and assuring a useful Pareto front approximation for the designer. In this work, a physical programming approach embedded in an evolutionary multi-objective optimization is presented as a tool for preference inclusion. The results presented and the algorithm developed validate the proposal as a potential tool for engineering design by means of evolutionary multi-objective optimization.This work was partially supported by the FPI-2010/19 grant and the PAID-2011/2732 project from the Universitat Polittccnica de Valencia and the projects TIN2011-28082 and ENE2011-25900 from the Spanish Ministry of Economy and Competitiveness.Reynoso Meza, G.; Sanchís Saez, J.; Blasco Ferragud, FX.; Garcia Nieto, S. (2014). Physical programming for preference driven evolutionary multi-objective optimization. Applied Soft Computing. 24:341-362. https://doi.org/10.1016/j.asoc.2014.07.009S3413622

Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization

[EN] Synthetic biology exploits the of mathematical modeling of synthetic circuits both to predict the behavior of the designed synthetic devices, and to help on the selection of their biological coin portents. The increasing complexity of the circuits being designed requires performing approximations and model reductions to get handy models. Parameter estimation in these models remains a challenging problem that has usually been addressed by optimizing the weighted combination of different prediction errors to obtain a single solution. The single-objective approach is inadequate to incorporate different kinds of experiments, and to identify parameters for an ensemble of biological circuit models. We present a methodology based on multi-objective optimization to perform parameter estimation that can fully harness to ensembles of local models for biological circuits. The methodology uses a global multi-objective evolutionary algorithm and a multi-criteria decision making strategy to select the most suitable solutions. Our approach finds an approximation to the Pareto optimal set of model parameters that correspond to each experimental scenario. Then, the Pareto set was clustered according to the experimental scenarios. This, in turn, allows to analyze the sensitivity of model parameters for different scenarios. Finally, we show the methodology applicability through the case study of a genetic incoherent feed-forward circuit, under different concentrations of the inducer input signal. (C) 2016 IFAC (International Federation Of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.This work is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1). Y.B. thanks grant FP/2013-3242 of Universitat Politecnica de Valencia and Becas Iberoamerica of Santander Group, Spain 2015. G.R.M. thanks the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil. A.V. thanks the Max Planck Society, the CSBD and the MPI-CBG. We are grateful to Dr. C,Bauerl and Dr, D. Provencio at the SB2CLab for their help in plasmid construction and getting experimental data. Also to Dr. V. Monedero at IATACSIC for allowing us to use the POLARstar plate reader at his lab,Boada-Acosta, YF.; Vignoni, A.; Reynoso Meza, G.; Picó, J. (2016). Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization. IFAC-PapersOnLine. 49(26):77-82. https://doi.org/10.1016/j.ifacol.2016.12.106S7782492

La influencia del COVID - 19, como hecho fortuito, sobre los contratos de arrendamiento de vivienda en el Distrito de Huánuco, 2021

Es de conocimiento público que a raíz de la propagación de la enfermedad del Covid-19, acontecieron tanto en nuestro país como en todo el mundo, múltiples problemas, que conllevaron a tomar medidas de emergencia; tanto en la salud, en la economía, entre otros; tan es así que en el presente trabajo lo que se analizó fue la influencia que tuvo esta enfermedad sobre los contratos de arrendamiento de vivienda, que si bien fueron fijados mucho antes de la propagación de esta enfermedad; sin embargo, se vieron afectados con ella; convirtiéndose en un hecho fortuito, que ha influido en gran magnitud el hecho que los arrendatarios puedan cumplir con el pago pactado en el contrato de arrendamiento, ello debido, ya sea por la pérdida o suspensión de su empleo, recorte de sus remuneraciones, entre otras situaciones; se entiende entonces que ante la emergencia sanitaria que aún vivimos, se ha tenido que ver la forma de darle una solución a este problema. De ahí que con la presente investigación lo que se buscó, fue analizar dicho problema a efectos de determinar lo que sucede cuando el arrendatario no puede cumplir con su obligación del pago de renta por un hecho imprevisible e inevitable, como el Covid-19, analizar si es posible que se dé un tema de renegociación frente a este tipo de problemas inesperado u otro tipo de soluciones frente a este tipo de acontecimientos inesperados; ante ello nos dimos cuenta de que nuestra legislación no nos establece solución alguna al respecto, de ahí la razón de la presente investigación

Comparison of design concepts in multi-criteria decision-making using level diagrams

[EN] In this work, we address the evaluation of design concepts and the analysis of multiple Pareto fronts in multi-criteria decision-making using level diagrams. Such analysis is relevant when two (or more) design concepts with different design alternatives lie in the same objective space, but describe different Pareto fronts. Therefore, the problem can be stated as a Pareto front comparison between two (or more) design concepts that only differ in their relative complexity, implementation issues, or the theory applied to solve the problem at hand. Such analysis will help the decision maker obtain a better insight of a conceptual solution and be able to decide if the use of a complex concept is justified instead of a simple concept. The approach is validated in a set of multi-criteria decision making benchmark problems. © 2012 Elsevier Inc. All rights reserved.This work was partially supported by the FPI-2010/19 Grant and Project PAID-06-11 from the Universitat Politecnica de Valencia and by Projects ENE2011-25900, TIN2011-28082 (Spanish Ministry of Science and Innovation) and GV/2012/073, PROMETEO/2012/028 (Generalitat Valenciana).Reynoso Meza, G.; Blasco Ferragud, FX.; Sanchís Saez, J.; Herrero Durá, JM. (2013). Comparison of design concepts in multi-criteria decision-making using level diagrams. INFORMATION SCIENCES. 221(1):124-141. https://doi.org/10.1016/j.ins.2012.09.049S124141221

PID controller tuning for unstable processes using a multi-objective optimisation design procedure

[EN] Multi-objective optimisation techniques have shown to be a useful tool for controller tuning applications. Such techniques are useful when: 1) it is difficult to find a controller with a desirable trade-off between conflictive objectives; or 2) it is valuable to extract an additional knowledge from the process by analysing track-off among possible controllers. In this work, we propose a. multi-objective optimisation design procedure for unstable process, using PID controllers. The provided examples show the usability of the procedure for this kind of process, sometimes difficult to control; comparison with existing tuning rule methods provide promising results for this tuning procedure. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.This work was partially supoted by projects FEDER-CICYT DPI2014-55276-C5-1 (Spain) and the fellowships FPI/2013-3242 (UPV, Spain), BJT-304804/2014-2 (CNpQ, Brazil). Second author wishes to thank to Universidad del Papaloapan by the approved project entitled Sintonizacin de controladores lineales optimo-robustos mediante algoritmos evolutivos y tecnicas de decision multi-criterio. Third author thanks the Santander Scholarship program (Investigacion-JPI).Reynoso-Meza, G.; Carrillo Ahumada, J.; Boada-Acosta, YF.; Picó, J. (2016). PID controller tuning for unstable processes using a multi-objective optimisation design procedure. IFAC-PapersOnLine. 49(7):284-289. https://doi.org/10.1016/j.ifacol.2016.07.287S28428949

Enhancing controller's tuning reliability with multi-objective optimisation: From Model in the loop to Hardware in the loop

[EN] In general, the starting point for the complex task of designing a robust and efficient control system is the use of nominal models that allow to establish a first set of parameters for the selected control scheme. Once the initial stage of design is achieved, control engineers face the difficult task of Fine-Tuning for a more realistic environment, where the environment conditions are as similar as possible to the real system. For this reason, in the last decades the use of Hardware-in-The-Loop (HiL) systems has been introduced. This simulation technique guarantees realistic simulation environments to test the designs but without danger of damaging the equipment. Also, in this iterative process of Fine-Tuning, it is usual to use different (generally conflicting/opposed) criteria that take into account the sensitivities that always appear in every project, such as economic, security, robustness, performance, for example. In this framework, the use of multi-objective techniques are especially useful since they allow to study the different design alternatives based on the multiple existing criteria. Unfortunately, the combination of multi-objective techniques and verification schemes based on Hardware-In-The-Loop presents a high incompatibility. Since obtaining the optimal set of solutions requires a high computational cost that is greatly increased when using Hardware- In-the-Loop. For this reason, it is often necessary to use less realistic but more computationally efficient verification schemes such as Model in the Loop (MiL), Software in the Loop (SiL) and Processor in the Loop (PiL). In this paper, a combined methodology is presented, where multi-objective optimisation and multi-criteria decision making steps are sequentially performed to achieve a final control solution. The authors claim that while going towards the optimisation sequence over MiL -> SiL -> PiL -> HiL platforms, the complexity of the problem is unveiled to the designer, allowing to state meaningful design objectives. In addition, safety in the step between simulation and reality is significantly increased.The authors would like to acknowledge the Spanish Ministry of Economy and Competitiveness for providing funding through the project DPI2015-71443-R and the grant BES-2012-056210. This work has been partially supported by the National Council of Scientific and Technological Development of Brazil (CNPq) through the BJT/304804/2014-2 and PQ-2/304066/2016-8 grants.Reynoso Meza, G.; Velasco-Carrau, J.; Garcia-Nieto, S.; Blasco, X. (2017). Enhancing controller's tuning reliability with multi-objective optimisation: From Model in the loop to Hardware in the loop. Engineering Applications of Artificial Intelligence. 64:52-66. https://doi.org/10.1016/j.engappai.2017.05.005S52666

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Multi-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applica- tions. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to en- hance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorpo- rated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark exam- ple in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques.This work was partially supported by the fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) and by EVO-CONTROL project (ref. PROMETEO/2012/028, Generalitat Valenciana - Spain).Reynoso Meza, G.; Sanchís Saez, J.; Blasco, X.; Freire, RZ. (2016). Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning. Expert Systems with Applications. 51:120-133. doi:10.1016/j.eswa.2015.11.028S1201335

Controller tuning by means of multi-objective optimization algorithms: a global tuning framework

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A holistic multi-objective optimization design technique for controller tuning is presented. This approach gives control engineers greater flexibility to select a controller that matches their specifications. Furthermore, for a given controller it is simple to analyze the tradeoff achieved between conflicting objectives. By using the multi-objective design technique it is also possible to perform a global comparison between different control strategies in a simple and robust way. This approach thereby enables an analysis to be made of whether a preference for a certain control technique is justified. This proposal is evaluated and validated in a nonlinear multiple-input multiple-output system using two control strategies: a classical proportional- integral-derivative control scheme and a feedback state controller.This work was supported in part by the FPI-2010/19 Grant and the Project PAID-06-11 from the Universitat Politecnica de Valencia and in part by the Projects DPI2008-02133, TIN2011-28082, and ENE2011-25900 from the Spanish Ministry of Science and Innovation.Reynoso Meza, G.; García-Nieto Rodríguez, S.; Sanchís Saez, J.; Blasco, X. (2013). Controller tuning by means of multi-objective optimization algorithms: a global tuning framework. IEEE Transactions on Control Systems Technology. 21(2):445-458. https://doi.org/10.1109/TCST.2012.2185698S44545821

Influence of hyper-parameters in algorithms based on Differential Evolution for the adjustment of PID-type controllers in SISO processes through mono and multi-objective optimisation

[EN] PID Controllers remain as the reliable front-line solution in feedback control loops. Even when their simplicity is one of the main reasons for this, the right tuning of their parameters is essential to guarantee their performance. As consequence, several tuning methods are available. Nowadays performing a tuning process via stochastic optimisation is an attractive solution for complex processes. Nevertheless, the solution obtained using such optimisation methods is very sensitive to the hyper-parameters used. In this paper, we propose to designers a set of hyper-parameters for different algorithms based on Differential Evolution in SISO processes. Obtained results show several aspects to consider regarding the most promising values for several optimisation instances, facilitating knowledge transfer for new optimisation instances.[ES] Los controladores PID se mantienen como una solución confiable de primera línea en sistemas de control retroalimentado. Incluso cuando su sencillez es una de las principales razones de ello, un correcto ajuste de sus parámetros es fundamental para garantizar un rendimiento satisfactorio. Como consecuencia, se encuentran disponibles varios métodos de ajuste. En la actualidad, realizar un proceso de ajuste mediante optimización estocástica es una solución atractiva para controlar procesos complejos. No obstante, la solución obtenida con estos métodos de optimización es muy sensible a los hiper-parámetros utilizados. En este artículo proponemos a los diseñadores un conjunto de hiper-parámetros para configurar diferentes algoritmos basados en Evolución Diferencial en sistemas de una entrada y una salida (SISO). Los resultados obtenidos muestran varios aspectos a considerar sobre los valores más prometedores para varias instancias de optimización facilitando la transferencia de conocimiento para nuevas instancias de optimización.Trabajo financiado parcialmente por el Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), y la Fundação Araucária (FAPPR) - Brasil - proyectos 310079/2019-5-PQ2, 4408164/2021-2-Univ y PRONEX-51432/2018-PPP.Martínez-Luzuriaga, PN.; Reynoso-Meza, G. (2022). Influencia de los hiper-parámetros en algoritmos basados en Evolución Diferencial para el ajuste de controladores del tipo PID en procesos SISO. Revista Iberoamericana de Automática e Informática industrial. 20(1):44-55. https://doi.org/10.4995/riai.2022.16517445520

Computing Optimal Distances to Pareto Sets of Multi-Objective Optimization Problems in Asymmetric Normed Lattices

[EN] Given a finite dimensional asymmetric normed lattice, we provide explicit formulae for the optimization of the associated (non-Hausdorff) asymmetric distance among a subset and a point. Our analysis has its roots and finds its applications in the current development of effective algorithms for multi-objective optimization programs. We are interested in providing the fundamental theoretical results for the associated convex analysis, fixing in this way the framework for this new optimization tool. The fact that the associated topology is not Hausdorff forces us to define a new setting and to use a new point of view for this analysis. Existence and uniqueness theorems for this optimization are shown. Our main result is the translation of the original abstract optimal distance problem to a clear optimization scheme. Actually, this justifies the algorithms and shows new aspects of the numerical and computational methods that have been already used in visualization of multi-objective optimization problems.This work was supported by the Ministerio de Economia y Competitividad (Spain) under grants DPI2015-71443-R and MTM2016-77054-C2-1-P.Blasco, X.; Reynoso-Meza, G.; Sánchez Pérez, EA.; Sánchez Pérez, JV. (2019). Computing Optimal Distances to Pareto Sets of Multi-Objective Optimization Problems in Asymmetric Normed Lattices. Acta Applicandae Mathematicae. 159(1):75-93. https://doi.org/10.1007/s10440-018-0184-z7593159