Differential Evolution in Robust Optimization Over Time Using a Survival Time Approach

Guzmán-Gaspar, José-Yaír; Mezura-Montes, Efrén; Domínguez-Isidro, Saúl

doi:10.3390/mca25040072

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…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.…”

Section: A Finding Robust Solutions Based On Predicted Fitness Of Can...mentioning

confidence: 99%

Robust Optimization Over Time: A Critical Review

Yazdani,

Omidvar,

Yazdani

et al. 2024

IEEE Trans. Evol. Computat.

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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

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Section: A Finding Robust Solutions Based On Predicted Fitness Of Can...mentioning

confidence: 99%

Robust Optimization Over Time: A Critical Review

Yazdani,

Omidvar,

Yazdani

et al. 2024

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Robust Optimization Method Based on Two-Stage Evaluation and Its Application in Optimal Scheduling of Integrated Energy System

Zhou,

2024

Applied Sciences

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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.

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“…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.…”

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confidence: 99%