Comparison of Representative Heuristic Algorithms for Multi-Objective Reservoir Optimal Operation

Wenzhuo Wang, Benyou Jia (), Slobodan P. Simonovic, Shiqiang Wu, Ziwu Fan and Li Ren
Additional contact information
Wenzhuo Wang: Hohai University
Benyou Jia: Nanjing Hydraulic Research Institute
Slobodan P. Simonovic: Western University
Shiqiang Wu: Nanjing Hydraulic Research Institute
Ziwu Fan: Nanjing Hydraulic Research Institute
Li Ren: Hohai University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 9, No 3, 2762 pages

Abstract: Abstract Heuristic algorithms (HAs) are widely used in multi-objective reservoir optimal operation (MOROO) due to the rapidity of the calculation and simplicity of their design. The literature usually focuses on one or two categories of HAs and simply reviews the state of the art. To provide an overall understanding and a specific comparison of HAs in MOROO, differential evolution (DE), particle swarm optimisation (PSO), and artificial physics optimisation (APO), which serve as typical examples of the three categories of HAs, are compared in terms of the development and applications using a designed experiment. Besides, the general model with constraints and fitness function, and the solution process using a hybrid feasible domain restoration method and penalty function method are also presented. Taking a designed experiment with multiple scenarios, the mean average of the optimal objective function values, the standard deviation of optimal objective function values, the mean average of the computational time, and population diversity are used for comparisons. Results of the comparisons show that (a) the problem of optimal multipurpose reservoir long-term operation is a mathematic programming problem with narrow feasible region and monotonic objective function; (b) it is easy to obtain the same optimal objective function value, but different optimal solutions using HAs; and (c) comparisons do not result in a clear winner, but DE can be more appropriate for MOROO.

Keywords: Artificial physics optimisation; Differential evolution; Multi-objective reservoir optimal operation; Particle swarm optimisation; Representative heuristic algorithm comparison (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-021-02864-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:35:y:2021:i:9:d:10.1007_s11269-021-02864-w

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-021-02864-w

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().