Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 2/2023

20-02-2021 | S.I. : 2019 India Intl. Congress on Computational Intelligence

The effect of different stopping criteria on multi-objective optimization algorithms

Authors: Iyad Abu Doush, Mohammed El-Abd, Abdelaziz I. Hammouri, Mohammad Qasem Bataineh

Published in: Neural Computing and Applications | Issue 2/2023

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Evolutionary multi-objective optimization (EMO) refers to the domain in which an evolutionary algorithm is applied to tackle an optimization problem with multiple objective functions. The literature is rich with many approaches proposed to solve multi-objective problems including the NSGA-II, MOEA/D, and MOPSO algorithms. The proposed approaches include stand-alone as well as hybrid techniques. One critical aspect of any evolutionary algorithm (EA) is the stopping criterion. The selection of a specific stopping criterion can have a considerable effect on the performance and the final solution provided by the EA. A number of different stopping criteria, specifically designed for EMO, have been proposed in the literature. In this paper, the performance of six different EMO algorithms is tested and compared using four stopping criteria. The experiments are performed using the ZDT, DTLZ, CEC2009, Tanaka and Srivana test functions. Experimental results are analyzed to highlight the proper stopping criteria for different algorithms.
previous article Hypothesis derivation and its verification by a wholly automated many-objective evolutionary optimization system
next article A flexible framework for anomaly Detection via dimensionality reduction

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Footnotes
Literature
1.
2.
AbuDoush I, Bataineh MQ (2015) Hybedrized NSGA-II and MOEA/D with Harmony search algorithm to solve multi-objective optimization problems. Springer, Berlin, pp 606–614
3.
Al-Betar MA, Doush IA, Khader AT, Awadallah MA (2012) Novel selection schemes for harmony search. Appl Math Comput 218(10):6095–6117MATH
4.
Audet C, Bigeon J, Cartier D, Le Digabel S, Salomon L (2018) Performance indicators in multiobjective optimization. Optimization Online
5.
Brockhoff D, Wagner T, Trautmann H (2015) 2 indicator-based multiobjective search. Evolution Comput 23(3):369–395CrossRef
6.
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, HobokenMATH
7.
Deb K, Jain H (2002) Running performance metrics for evolutionary multi-objective optimization. In: Simulated Evolution and Learning (SEAL), pp 13–20
8.
Deb K, Jain H (2012) Handling many-objective problems using an improved nsga-ii procedure. IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8
9.
Deb K, Pratap A, Agarwal S, Meyarivan T (2002a) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evolution Comput 6(2):182–197CrossRef
10.
Deb K, Thiele L, Laumanns M, Zitzler E (2002b) Scalable multi-objective optimization test problems. In: Proceedings of the Congress on Evolutionary Computation (CEC-2002), Honolulu, USA, pp 825–830
11.
Deb K, Thiele L, Laumanns M, Zitzler E (2005) Scalable test problems for evolutionary multiobjective optimization. In: Evolutionary multiobjective optimization, Springer, pp 105–145
12.
Doush IA, Bataineh MQ (2015) Hybedrized nsga-ii and moea/d with harmony search algorithm to solve multi-objective optimization problems. In: International conference on neural information processing, Springer, pp 606–614
13.
Doush IA, Bataineh MQ, El-Abd M (2017) The hybrid framework for multi-objective evolutionary optimization based on harmony search algorithm. In: First international conference on real time intelligent systems, Springer, pp 134–142
14.
Doush IA, Bataineh MQ, El-Abd M (2019) On different stopping criteria for multi-objective harmony search algorithms. In: Proceedings of the 2019 3rd international conference on intelligent systems, metaheuristics and swarm intelligence, pp 30–34
15.
Doush IA, Alrashdan WB, Al-Betar MA, Awadallah MA (2020) Community detection in complex networks using multi-objective bat algorithm. Int J Math Modell Numer Optim 10(2):123–140
16.
Durillo JJ, Nebro AJ (2011) jmetal: a java framework for multi-objective optimization. Adv Eng Softw 42(10):760–771CrossRef
17.
18.
Farag M, Mousa A, El-Shorbagy M, El-Desoky I (2020) A new hybrid metaheuristic algorithm for multiobjective optimization problems. Int J Comput Intell Syst 13(1):920–940CrossRef
19.
Gutjahr WJ, Pichler A (2016) Stochastic multi-objective optimization: a survey on non-scalarizing methods. Ann Operat Res 236(2):475–499MathSciNetCrossRefMATH
20.
Ishibuchi H, Murata T (1998) A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans Syst Man Cybern Part C Appl Rev 28(3):392–403CrossRef
21.
Ji J, Weng Y, Yang C (2020) A new diversity maintenance strategy based on the double granularity grid for multiobjective optimization. In: ICPRAM, pp 88–95
22.
Kadhar KMA, Baskar S (2018) A stopping criterion for decomposition-based multi-objective evolutionary algorithms. Soft Comput 22(1):253–272CrossRef
23.
Ks L, Zw G, (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3933CrossRef
24.
Li H, Zhang Q (2009) Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii. IEEE Trans Evolut Comput 13(2):284–302CrossRef
25.
Luque M, Miettinen K, Ruiz AB, Ruiz F (2012) A two-slope achievement scalarizing function for interactive multiobjective optimization. Comput Oper Res 39(7):1673–1681MathSciNetCrossRefMATH
26.
Martí L, García J, Berlanga A, Molina JM (2007) A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithms. In: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, pp 2835–2842
27.
Marti L, García J, Berlanga A, Molina JM, (2009) An approach to stopping criteria for multi-objective optimization evolutionary algorithms: the mgbm criterion. In: IEEE Congress on Evolutionary Computation, CEC’09, IEEE, pp 1263–1270
28.
Martí L, García J, Berlanga A, Molina JM (2010) A progress indicator for detecting success and failure in evolutionary multi-objective optimization. In: IEEE congress on evolutionary computation, IEEE, pp 1–8
29.
Marti L, García J, Berlanga A, Molina JM, (2010) A progress indicator for detecting success and failure in evolutionary multi-objective optimization. In: IEEE congress on evolutionary computation, CEC’10., IEEE, pp 1–8
30.
Marti L, García J, Berlanga A, Molina JM (2016) A stopping criterion for multi-objective optimization evolutionary algorithms. Inform Sci 367–368:700–718CrossRefMATH
31.
Panda A, Pani S (2016) A symbiotic organisms search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems. Appl Soft Comput 46:344–360CrossRef
32.
Rangaiah GP, Feng Z, Hoadley AF (2020) Multi-objective optimization applications in chemical process engineering: Tutorial and review. Processes 5(5):155–173
33.
Reyes Sierra M, Coello Coello CA (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intell Res 2(3):287–308MathSciNet
34.
Ricart J, Hüttemann G, Lima J, Barán B (2011) Multiobjective harmony search algorithm proposals. Electron Notes Theor Comput Sci 281:51–67CrossRef
35.
Sharma S, Rangaiah GP (2013) An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes. Comput Chem Eng 56(2):155–173CrossRef
36.
Sharma S, Rangaiah GP (2013) An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes. Comput Chem Eng 56:155–173CrossRef
37.
Sindhya K, Deb K, Miettinen K (2011) Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm. Nat Comput 10(4):1407–1430MathSciNetCrossRefMATH
38.
Sindhya K, Miettinen K, Deb K (2013) A hybrid framework for evolutionary multi-objective optimization. IEEE Trans Evolut Comput 17(4):495–511CrossRefMATH
39.
Wagner T, Trautmann H (2010) Online convergence detection for evolutionary multi-objective algorithms revisited. In: IEEE congress on evolutionary computation, IEEE, pp 1–8
40.
Wagner T, Trautmann H, Martí L (2011) A taxonomy of online stopping criteria for multi-objective evolutionary algorithms. In: International conference on evolutionary multi-criterion optimization, Springer, pp 16–30
41.
Wagner T, Trautmann H, Martí L (2011) A taxonomy of online stopping criteria for multi-objective evolutionary algorithms. In: Evolutionary multi-criterion optimization, Springer, pp 16–30
42.
Wong YQJ, Sharma S, Rangaiah GP (2016) Design of shell-and-tube heat exchangers for multiple objectives using elitist non-dominated sorting genetic algorithm with termination criteria. Appl Thermal Eng 93(290):888–899CrossRef
43.
Zapotecas Martinez S, Coello Coello CA (2012) A direct local search mechanism for decomposition-based multi-objective evolutionary algorithms. IEEE congress on evolutionary computation (CEC), IEEE, pp 1–8
44.
Zhang Q, Li H (2007) Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6):712–731CrossRef
45.
Zhang Q, Zhou A, Zhao S, Suganthan PN, Liu W, Tiwari S (2008) Multiobjective optimization test instances for the cec 2009 special session and competition. University of Essex, Colchester, UK and Nanyang Technological University, Singapore, Special Session on Performance Assessment of Multi-Objective Optimization Algorithms, Technical Report
46.
Zhang Q, Liu W, Li H (2009) The performance of a new version of moea/d on cec09 unconstrained mop test instances. In: IEEE congress on evolutionary computation, pp 203–208
47.
Zhao S, Suganthan PN, Zhang Q (2012) Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE Trans Evolut Comput 16(3):442–446CrossRef
48.
Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: Empirical results. Evolut Comput 8(2):173–195CrossRef
Metadata
Title
The effect of different stopping criteria on multi-objective optimization algorithms
Authors
Iyad Abu Doush
Mohammed El-Abd
Abdelaziz I. Hammouri
Mohammad Qasem Bataineh
Publication date
20-02-2021
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 2/2023
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-05805-1

Other articles of this Issue 2/2023

Neural Computing and Applications 2/2023 Go to the issue

Original Article

Adapting deep learning models between regional markets

Original Article

Evaluating the performance of meta-heuristic algorithms on CEC 2021 benchmark problems

Original Article

Convolutional neural networks combined with Runge–Kutta methods

S.I. : 2019 India Intl. Congress on Computational Intelligence

Fake review and reviewer detection through behavioral graph partitioning integrating deep neural network

Original Article

Knowledge tracing based on multi-feature fusion

Original Article

Generating ten BCI commands using four simple motor imageries and classification by divergence-based DNN

Premium Partner

    Image Credits