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This study presents an empirical comparison of the standard differential evolution (DE) against three random sampling methods to solve robust optimization over time problems with a survival time approach to analyze its viability and performance capacity of solving problems in dynamic environments. A set of instances with four different dynamics, ge...
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Robust optimization over time (ROOT) is a relatively recent topic in the field of dynamic evolutionary optimization (EDO). The goal of ROOT problems is to find the optimal solution for several environments at the same time. Although significant contributions to ROOT have been published in the past, it is not clear to what extent progress has been m...
Citations
... Zhang et al. [21] studied the prediction model under the ROOT framework. Guzmán-Gaspar et al. [22] made an empirical comparison between the DE algorithm and random sampling method and analyzed the feasibility and effectiveness of the differential evolution algorithm to solve the modified ROOT problem in dynamic environments by using the survival time method. Yazdani et al. [23] proposed a multi-population ROOT and introduce two metrics, one of which is to estimate the robust estimation component of the promising region, and the other is the dual-mode computing resource allocation component considering various factors such as robustness. ...
As an emerging energy allocation method, shared energy storage devices play an important role in modern power systems. At the same time, with the continuous improvement in renewable energy penetration, modern power systems are facing more uncertainties from the source side. Therefore, a robust optimization algorithm that considers both shared energy storage devices and source-side uncertainty is needed. Responding to the above issues, this paper first establishes an optimal model of a regional integrated energy system with shared energy storage. Secondly, the uncertainty problem is transformed into a dynamic optimization problem with time-varying parameters, and a modified robust optimization over time algorithm combined with scenario analysis is proposed to solve such optimization problems. Finally, an optimal scheduling objective function with the lowest operating cost of the system as the optimization objective is established. In the experimental part, this paper first establishes a dynamic benchmark test function to verify the validity of proposed method. Secondly, the multi-mode actual verification of the proposed algorithm is carried out through a regional integrated energy system. The simulation results show that the modified robust optimization over time (ROOT) algorithm could find solutions with better robustness in the same dynamic environment based on the two-stage evaluation strategy. Compared with the existing algorithms, the average fitness and survival time of the robust solution obtained by the modified ROOT algorithm are increased by 94.41% and 179.78%. At the same time, the operating cost of the system is reduced by 11.65% by using the combined optimization scheduling method proposed in this paper.
... Using (10), the ROOT S Q method in [13] searches for solutions with higher estimated robustness since future acceptability of the candidate solutions are taken into account in the substitute objective function. In [64], the performance of the ROOT S Q method in [13] is investigated where DE is used as the optimization component. ...
Robust optimization over time (ROOT) is the combination of robust optimization and dynamic optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can be due to the high cost of switching between deployed solutions, limitations on the resources required to deploy new solutions, and/or the system’s inability to tolerate frequent changes in the deployed solutions. ROOT is dedicated to the study and development of algorithms capable of dealing with the implications of deploying or maintaining solutions over longer time horizons involving multiple environmental changes. This paper presents an in-depth review of the research on ROOT. The overarching aim of this survey is to help researchers gain a broad perspective on the current state of the field, what has been achieved so far, and the existing challenges and pitfalls. This survey also aims to improve accessibility and clarity by standardizing terminology and unifying mathematical notions used across the field, providing explicit mathematical formulations of definitions, and improving many existing mathematical descriptions. Moreover, we classify ROOT problems based on two ROOT-specific criteria: the requirements for changing or keeping deployed solutions and the number of deployed solutions. This classification helps researchers gain a better understanding of the characteristics and requirements of ROOT problems, which is crucial to systematic algorithm design and benchmarking. Additionally, we classify ROOT methods based on the approach they use for finding robust solutions and provide a comprehensive review of them. This survey also reviews ROOT benchmarks and performance indicators. Finally, we identify several future research directions.
... Adicionalmente, en la Tabla I se detalla qué contribuciones se han hecho por cada escenario. [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17] 2 ...
La optimización robusta en el tiempo es un tema relativamente reciente dentro de la computación evolutiva, para la que no existe hasta ahora una caracterización adecuada de la incertidumbre presente en la función objetivo del problema. Este trabajo propone una clasificación de este aspecto, y organiza las contribuciones actuales de acuerdo a las categorías de la clasificación propuesta. Algunas oportunidades de investigación son propuestas.
... In [6], Guzmán-Gaspar et al. present an empirical comparison of the standard differential evolution (DE) against three random sampling methods to solve particular robust ...
... In [6], Guzmán-Gaspar et al. present an empirical comparison of the standard differential evolution (DE) against three random sampling methods to solve particular robust optimization problems in dynamic environments. The findings indicate that DE is a suitable algorithm to deal with this type of dynamic search space when a survival time approach is considered. ...
Solving scientific and engineering problems from the real world is a very complicated task, currently; hence, the development of powerful search and optimization techniques is of great importance [...]
Robust optimization over time (ROOT) is a relatively recent topic in the field of dynamic evolutionary optimization (EDO). The goal of ROOT problems is to find the optimal solution for several environments at the same time. Although significant contributions to ROOT have been published in the past, it is not clear to what extent progress has been made in terms of the type of problem addressed. In particular, we believe that there is confusion regarding what it actually means to solve a ROOT problem. To overcome these limitations, the objective of this paper is twofold. On the one hand, to provide a characterization framework of ROOT problems in terms of their most relevant features, and on the other hand, to organize existing contributions according to it. As a result, from an initial set of 186 studies, the characterization framework was applied to 35 of them, allowing to identification of some important gaps and proposing new research opportunities. We have also experimentally addressed the effect of available information on ROOT problems, concluding that there is indeed a significant impact on the performance of the algorithm and that the proposed classification is appropriate to characterize the complexity of ROOT problems. To help identify further research opportunities, we have implemented an interactive dashboard with the results of the review conducted, which is available online.
Hoy en dia, utilizamos ampliamente los sistemas
de recomendacion en una gran variedad de problemas. Estos
sistemas de filtrado de informacion se han establecido como una
de las principales herramientas que ayudan a los usuarios en
situaciones cotidianas. Sin embargo, a medida que se desarrollan
nuevas herramientas de procesamiento de la informaci ́on, se
plantean nuevos y desafiantes escenarios donde aplicar estos
sistemas de recomendacion en areas como la salud, el ejercicio
fisico o el turismo.
En estas situaciones estaremos frente a lo que se ha denominado como sistemas de recomendacion complejos. En ellos
deberemos balancear la informaci ́on proveniente de de m ultiples
fuentes de informacion de items, usuarios o expertos, al mismo
tiempo que diferenciamos entre restricciones o limitaciones y
preferencias. Ademas, la recomendacion final estara formada por
un conjunto de elementos relacionados que debera construirse.
En este resumen se presenta un modelo teorico de sistema
de recomendacion para este tipo de situaciones, basado en
computacion evolutiva y recomendacion basada en contenido,
publicado en [1]
Energy loss due to the complete equipping and overstatement of infrastructure networks has recently prompted internet service providers to reduce energy consumption. Increasing energy costs and environmental awareness have recently raised concerns about grid power consumption and the underlying structure of services; therefore, one of the top priorities for service providers is to reduce energy consumption. In this paper, a method based on the differential evolution optimization algorithm is introduced to reduce the energy consumption costs, network load balancing, and execution time. To increase the convergence speed in the optimization algorithm, two Mamdani fuzzy inference systems have been used which are used in the optimization objective function to determine the fitness amount of each allocation. To better understand proposed algorithm behavior, the results of the differential evolution algorithm are compared with the AGA and MPGA methods. Simulation results show that the proposed algorithm achieves more than 30% performance improvement in comparison with the previous algorithms.
