Mathematical optimization versus Metaheuristic techniques: A performance comparison for reconfiguration of distribution systems

Silveira, Christoffer L. Bezão; Tabares, Alejandra; Faria, Lucas Teles

doi:10.1016/j.epsr.2021.107272

Cited by 30 publications

(12 citation statements)

References 55 publications

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“…As compared to classical methods, metaheuristic methods - such as the GWO – “although do not guarantee optimality, are suitable to solve the RDS problem, especially for large-size systems” [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

Enhancement of distribution system performance with reconfiguration, distributed generation and capacitor bank deployment

Jayabarathi,

Raghunathan,

Mithulananthan

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Enhancement of distribution system performance with reconfiguration, distributed generation and capacitor bank deployment

Jayabarathi,

Raghunathan,

Mithulananthan

et al. 2024

Heliyon

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“…There are also differences in the performance of metaheuristic and mathematical methods with respect to solution quality, computational time, the scale of the system studied, etc. The problem specificity of mathematical programming makes it relatively inflexible [16,17]. Comparisons among metaheuristic and mathematical programming optimization methods with their strengths and weaknesses are presented in Table S1 (in Supplementary Files).…”

Section: Multi-objective Optimization (Moo)mentioning

confidence: 99%

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

Ferdous,

Bensebaa,

Milani

et al. 2024

Sustainability

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The integration of Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) has gathered significant attention across various scientific research domains to facilitate integrated sustainability assessment. Recently, there has been a growing interest in hybrid approaches that combine MCDM with MOO, aiming to enhance the efficacy of the final decisions. However, a critical gap exists in terms of providing clear methodological guidance, particularly when dealing with data uncertainties. To address this gap, this systematic review is designed to develop a generic decision tree that serves as a practical roadmap for practitioners seeking to perform MOO and MCDM in an integrated fashion, with a specific focus on accounting for uncertainties. The systematic review identified the recent studies that conducted both MOO and MCDM in an integrated way. It is important to note that this review does not aim to identify the superior MOO or MCDM methods, but rather it delves into the strategies for integrating these two common methodologies. The prevalent MOO methods used in the reviewed articles were evolution-based metaheuristic methods. TOPSIS and PROMETHEE II are the prevalent MCDM ranking methods. The integration of MOO and MCDM methods can occur either a priori, a posteriori, or through a combination of both, each offering distinct advantages and drawbacks. The developed decision tree illustrated all three paths and integrated uncertainty considerations in each path. Finally, a real-world case study for the pulse fractionation process in Canada is used as a basis for demonstrating the various pathways presented in the decision tree and their application in identifying the optimized processing pathways for sustainably obtaining pulse protein. This study will help practitioners in different research domains use MOO and MCDM methods in an integrated way to identify the most sustainable and optimized system.

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“…The significant difference is that a heuristic solver can quickly find a solution; however, might be a local minimum and not a global one (Silveira et al, 2021). Silveira et al (2021) suggested that heuristic solvers were better suited for large-scale systems. The category solver contained global and heuristic as subcategories.…”

Section: Figurementioning

confidence: 99%

Design optimization of district heating systems: A review

2022

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District heating systems offer the possibility of lowering emissions and support the goal of reaching a carbon-neutral energy system by integrating renewable heat sources. Therefore, this work provided a systematic literature review to identify potential research gaps and show the literature distribution over the relevant topics. The focus is on the design optimization with (non-)linear programming of district heating systems in the context of decarbonization. Furthermore, crucial energy balance equations were extracted from the literature for a potential optimization problem. The systematic literature review limited its search to two databases, 10 years timespan, a quality measure, and uses keywords regarding topic and method. Categories were derived based on the subject and literature to cluster the found publications and identify potential research gaps. The results showed potential research gaps in the depiction of different stakeholder decisions, reduction of computational efforts, and their resulting uncertainties. Additionally, they identified gaps in the integration of low-grade heat sources, thermal storage facilities, and energy converters, especially geothermal energy, large-scale heat pumps, and seasonal storages.

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Mathematical optimization versus Metaheuristic techniques: A performance comparison for reconfiguration of distribution systems

Cited by 30 publications

References 55 publications

Enhancement of distribution system performance with reconfiguration, distributed generation and capacitor bank deployment

Enhancement of distribution system performance with reconfiguration, distributed generation and capacitor bank deployment

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

Design optimization of district heating systems: A review

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