A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm

Baquela, Enrique Gabriel; Olivera, Ana Carolina

doi:10.1016/j.orp.2019.100098

Cited by 13 publications

(7 citation statements)

References 27 publications

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“…The use of simulations eases dealing with such problems. However, the problem with Simulation‐Driven Optimization 53 is that it involves numerous simulations. Thus, it is expensive in terms of the time of convergence especially with problems where there are several objectives in conflict.…”

Section: Resultsmentioning

confidence: 99%

Smart system management and techno‐environmental optimal sizing of a desalination plant powered by renewables with energy storage

Affi

Cherif

2021

Int J Energy Res

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The present study assessed the performances of a brackish water reverse osmosis desalination unit powered by (PV/Wind/Battery) system with hydraulic storage. The hybrid renewable energy system and the reverse osmosis desalination plant were modeled for a specific site in southern Tunisia using real meteorological and water consumption data for 1 year. A total of 8760 simulations were treated under a dynamic simulator coupled to a smart energy management strategy to reach the hourly water load demand. Although the recognized reliability of the hybrid renewable sources compared to one-power source, their design may still critical. Therefore, in this paper, the authors suggest applying the methodology of optimizing the presented unit using an integrated systemic meta-modeling which is built through the design of experiments tool and defined by an equation involving the decision variables of the optimization as its parameters. This equation outlines the objects of the optimum sizing. The selected optimal design of the proposed unit is predicted by the genetic algorithm NSGA-II based on minimizing two objectives in conflict: Embodied energy and hydraulic loss of power supply. The CPU time of the NSGA-II optimization via the meta-model reached only 15 minutes of treatment (with one day to build the design of experiments) compared to the classic optimization via dynamic simulation. The results of the optimum sizing show that it is possible to consider a low water shortage rate (0% ⩽ LPSP-H < 5%) for a marginal variation of embodied energy rate which does not exceed (7%). K E Y W O R D S design of experiments, hybrid renewable energy, meta-model, multi-objective genetic algorithm, optimization, water desalination, water-energy nexus 1 | INTRODUCTION Climate change affects many sides of the environment and severely emphasizes the issue of water scarcity. Indeed, global warming attacks the planet's humidity and the water's underground basins, and if the water management strategies don't change on the behalf of "rescuing water sources", by 2050 the number of environmental refugees will be doubled five times than the actual number. Currently, the world population is about 7.6 billion, reaching 8.6 billion by 2030 and exceeding 9 billion in 2050 whom will be in increasing need of freshwater. 1,2 Desalting, as it was known during World War II while the freshwater supplies were restricted, is likely to be the

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Section: Resultsmentioning

confidence: 99%

Smart system management and techno‐environmental optimal sizing of a desalination plant powered by renewables with energy storage

Affi

Cherif

2021

Int J Energy Res

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“…Examples include applications related to the automotive (Fu and Sahin, 2004;Ivanova and Kuhnt, 2014;Stork et al, 2008), energy (Sharif and Hammad 2019;Storti et al, 2019), hydropower (Bananmah et al, 2020;Mooselu et al, 2019), and transit infrastructure (Yin et al, 2016) sectors. Many authors have also studied MBSO applied to resource allocation problems (e.g., (Coelho and Pinto 2018;Song et al, 2005;Yousefi and Yousefi 2019;Zeinali et al, 2015), to synthetic mathematical functions (e.g., Baquela and Olivera, 2019;Kim and Boukouvala, 2020;Gonzalez et al, 2020;Wang et al, 2020), and to urban traffic issues Chong and Osorio 2018;Osorio and Bierlaire 2013;Osorio and Chong 2015).…”

Section: Nature Of Researchmentioning

confidence: 99%

“…Although most studies addressed single-objective problems, 25.8% studied metamodeling for multi-objective optimization problems, mainly dealing with identifying Pareto Frontiers. We can observe the importance of using metamodeling, since recursive SO methods are practically unfeasible for dimensionality and solution space problems found in this SLR, in addition to proving to be good choices for multi objective optimizations, which are a class of NP-hard problems (Baquela and Olivera 2019).…”

Section: Nature Of Researchmentioning

confidence: 99%

“…Currently, several algorithms are used to build metamodels. Some examples include Response Surface Methodology (RSM) (Hassrnaayebi et al, 2019;Mahdavi;Amiri, 2011), the Kriging algorithm (Baquela and Olivera, 2019;Pedrielli et al, 2020), the Radial Basis Function (RBF) (Christelis and Mantoglou, 2016;Yin et al, 2016), and Artificial Neural Networks (ANN) (Raei et al, 2019;Storti et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Metamodel-Based Simulation Optimization: A Systematic Literature Review

Amaral¹,

Montevechi²,

Miranda³

2022

Uncertainty Modeling: Fundamental Concepts and Models

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Over the past few decades, metamodeling tools have received attention for their ability to represent and improve complex systems. The use of metamodeling techniques in optimization problems via simulation has grown considerably in recent years to promote more robust and agile decision-making, determining the best scenario among the solution space to the problem. In this sense, this Chapter discusses the state of the art of metamodeling-based simulation optimization, presenting the gaps, opportunities, and future perspectives found in literature.

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“…This reference point is usually the nadir point [47] that is specified in (10). The hyper‐volume of a Pareto front is expressed as [48]

HV = volume ()(, ⋃_{j = 1}^{| bold-italicS |} v_{j}),

where

| bold-italicS |

is the number of solutions in the approximated solution set

bold-italicS

v_{i}

is the volume between the nadir point and the solution j in the set

bold-italicS

. The higher the hyper‐volume, the better diversity and convergence of the solutions, and thus the better the algorithm performance [49].…”

Section: Simulations and Performance Evaluationmentioning

confidence: 99%

Bio‐inspired metaheuristic framework for clustering optimisation in VANETs

Alsuhli

Fahmy²,

Khattab³

2020

IET intell. transp. syst

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A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm

Cited by 13 publications

References 27 publications

Smart system management and techno‐environmental optimal sizing of a desalination plant powered by renewables with energy storage

Smart system management and techno‐environmental optimal sizing of a desalination plant powered by renewables with energy storage

Metamodel-Based Simulation Optimization: A Systematic Literature Review

Bio‐inspired metaheuristic framework for clustering optimisation in VANETs

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