Multi-objective pump scheduling optimisation using evolutionary strategies

被引：120
|
作者：
Barán, B [1 ]
von Lücken, C [1 ]
Sotelo, A [1 ]
机构：
[1] Natl Univ Asuncion, Natl Comp Ctr, POB 1439, San Lorenzo, Paraguay
来源：
ADVANCES IN ENGINEERING SOFTWARE | 2005年 / 36卷 / 01期
关键词：
pump scheduling; evolutionary computation; genetic algorithms; pareto dominance; multi-objective optimisation; water supply;
D O I：
10.1016/j.advengsoft.2004.03.012
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Multi-objective Evolutionary Algorithms (MOEAs) are used to solve an optimal pump-scheduling problem with four objectives to be minimized: electric energy cost, maintenance cost, maximum power peak, and level variation in a reservoir. Six different MOEAs were implemented and compared. In order to consider hydraulic and technical constraints, a heuristic algorithm was developed and combined with each implemented MOEA. Evaluation of experimental results of a set of metrics shows that the Strength Pareto Evolutionary Algorithm achieves better overall performance than other MOEAs for the parameters considered in the test problem, providing a wide range of optimal pump schedules to chose from. (C) 2004 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.
引用
收藏
页码：39 / 47
页数：9
相关论文
共 50 条
  • [1] Multi-objective optimisation of the pump scheduling problem using SPEA2
    López-Ibáñez, M
    Prasad, TD
    Paechter, B
    2005 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-3, PROCEEDINGS, 2005, : 435 - 442
  • [2] Multi-objective Evolutionary Algorithms Assessment for Pump Scheduling Problems
    Gutierrez-Bahamondes, Jimmy H.
    Salgueiro, Yamisleydi
    Mora-Melia, Daniel
    Alsina, Marco A.
    Silva-Rubio, Sergio A.
    Iglesias-Rey, Pedro L.
    2019 IEEE CHILEAN CONFERENCE ON ELECTRICAL, ELECTRONICS ENGINEERING, INFORMATION AND COMMUNICATION TECHNOLOGIES (CHILECON), 2019,
  • [3] Multi-Objective Evolutionary Beer Optimisation
    al-Rifaie, Mohammad Majid
    Cavazza, Marc
    PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 2022, : 683 - 686
  • [4] Evolutionary multi-objective optimisation: a survey
    Nedjah, Nadia
    Mourelle, Luiza de Macedo
    INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2015, 7 (01) : 1 - 25
  • [5] Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms
    Petrovski, A
    McCall, J
    EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PROCEEDINGS, 2001, 1993 : 531 - 545
  • [6] Robust design optimisation using multi-objective evolutionary algorithms
    Lee, D. S.
    Gonzalez, L. F.
    Periaux, J.
    Srinivas, K.
    COMPUTERS & FLUIDS, 2008, 37 (05) : 565 - 583
  • [7] RSVP performance evaluation using multi-objective evolutionary optimisation
    Komolafe, O
    Sventek, J
    IEEE Infocom 2005: The Conference on Computer Communications, Vols 1-4, Proceedings, 2005, : 2447 - 2457
  • [8] Evolutionary Dynamic Multi-objective Optimisation: A Survey
    Jiang, Shouyong
    Zou, Juan
    Yang, Shengxiang
    Yao, Xin
    ACM COMPUTING SURVEYS, 2023, 55 (04)
  • [9] Multi-objective evolutionary optimisation of microwave oscillators
    Brito, LDC
    de Carvalho, P
    Bermúdez, LA
    ELECTRONICS LETTERS, 2004, 40 (11) : 677 - 678
  • [10] On the Effect of Populations in Evolutionary Multi-Objective Optimisation
    Giel, Oliver
    Lehre, Per Kristian
    EVOLUTIONARY COMPUTATION, 2010, 18 (03) : 335 - 356
12345