Skip to main content
Erschienen in:

24.03.2022 | Original Article

A novel enhanced global exploration whale optimization algorithm based on Lévy flights and judgment mechanism for global continuous optimization problems

verfasst von: Jianxun Liu, Jinfei Shi, Fei Hao, Min Dai

Erschienen in: Engineering with Computers | Ausgabe 4/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Whale optimization algorithm (WOA) is a very popular meta-heuristic algorithm. When optimizing complex multi-dimensional problems, the WOA has problems such as poor convergence behavior and low exploration efficiency. To improve the convergence behavior of the WOA and strengthen its global exploration efficiency, we propose a novel enhanced global exploration whale optimization algorithm (EGE-WOA). First, Lévy flights have the ability to strengthen global space search. For unconstrained optimization problems and constrained optimization problems, the EGE-WOA introduces Lévy flights to enhance its global exploration efficiency. Then, the EGE-WOA improves its convergence behavior by introducing new convergent dual adaptive weights. Finally, according to the characteristics of sperm whales hunting by emitting high-frequency ultrasound, the EGE-WOA introduces a new mechanism for judging the predation status of whales. The judgment mechanism is to judge the three predation states of whales by judging the fitness value between the optimal whale individual and any whale individual. The proposed new judgment mechanism can indeed effectively improve the global exploration efficiency of the WOA. For the exploration efficiency of the unconstrained optimization problems and constrained optimization problems, the EGE-WOA combines the Lévy flights and judgment mechanism in different ways to achieve efficient exploration efficiency and better convergence behavior. The experimental results show that in the optimization process of 33 unconstrained benchmark functions and 6 constrained real cases, the mean and standard deviation of the EGE-WOA are better than other algorithms.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

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!

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!

Literatur
1.
Zurück zum Zitat Chavan PP, Rani BS, Murugan M, Chavan P (2020) A novel image compression model by adaptive vector quantization: modified rider optimization algorithm. Sadhana Acad Proc Eng Sci 45(1):1–15MathSciNet Chavan PP, Rani BS, Murugan M, Chavan P (2020) A novel image compression model by adaptive vector quantization: modified rider optimization algorithm. Sadhana Acad Proc Eng Sci 45(1):1–15MathSciNet
2.
Zurück zum Zitat Montiel O, Sepúlveda R, Orozco-Rosas U (2015) Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field. J Intell Rob Syst 79(2):237–257CrossRef Montiel O, Sepúlveda R, Orozco-Rosas U (2015) Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field. J Intell Rob Syst 79(2):237–257CrossRef
3.
Zurück zum Zitat Mortazavi A (2021) Solving structural optimization problems with discrete variables using interactive fuzzy search algorithm. Struct Eng Mech 79(2):247–265MathSciNet Mortazavi A (2021) Solving structural optimization problems with discrete variables using interactive fuzzy search algorithm. Struct Eng Mech 79(2):247–265MathSciNet
4.
Zurück zum Zitat Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bioinspir Comput 2(2):78–84 CrossRef Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bioinspir Comput 2(2):78–84 CrossRef
5.
Zurück zum Zitat Oliva D, Aziz MAE, Hassanien AE (2017) Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl Energy 200:141–154CrossRef Oliva D, Aziz MAE, Hassanien AE (2017) Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl Energy 200:141–154CrossRef
6.
Zurück zum Zitat Kaveh A, Ghazzan MI (2017) Enhanced Whale optimization algorithm for sizing optimization of skeletal structures. Mech Based Des Struct Mach 45(3):345–362CrossRef Kaveh A, Ghazzan MI (2017) Enhanced Whale optimization algorithm for sizing optimization of skeletal structures. Mech Based Des Struct Mach 45(3):345–362CrossRef
7.
Zurück zum Zitat Prakash DB, Lakshminarayana C (2016) Optimal siting of capacitors in radial distribution network using whale optimization algorithm. Alex Eng J 56(4):499–509CrossRef Prakash DB, Lakshminarayana C (2016) Optimal siting of capacitors in radial distribution network using whale optimization algorithm. Alex Eng J 56(4):499–509CrossRef
8.
Zurück zum Zitat Reddy PDP, Reddy VCV, Manohar TG (2017) Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems. Renew Wind Water Solar 4(1):3CrossRef Reddy PDP, Reddy VCV, Manohar TG (2017) Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems. Renew Wind Water Solar 4(1):3CrossRef
9.
Zurück zum Zitat Maeda K, Fukano Y, Yamamichi S, Nitta D, Kurata H (2011) An integrative and practical evolutionary optimization for a complex, dynamic model of biological networks. Bioprocess Biosyst Eng 34(4):433–446CrossRef Maeda K, Fukano Y, Yamamichi S, Nitta D, Kurata H (2011) An integrative and practical evolutionary optimization for a complex, dynamic model of biological networks. Bioprocess Biosyst Eng 34(4):433–446CrossRef
10.
Zurück zum Zitat Goldfeld SM, Quandt RE, Trotter HF (1996) Maximization by quadratic hill-climbing. Econ J Econ Soc 541–551 Goldfeld SM, Quandt RE, Trotter HF (1996) Maximization by quadratic hill-climbing. Econ J Econ Soc 541–551
11.
Zurück zum Zitat Abbasbandy S (2003) Improving Newton–Raphson method for nonlinear equations by modified adomian decomposition method. Appl Math Comput 145(2-3):887–893MathSciNetMATH Abbasbandy S (2003) Improving Newton–Raphson method for nonlinear equations by modified adomian decomposition method. Appl Math Comput 145(2-3):887–893MathSciNetMATH
13.
Zurück zum Zitat Birdi J, Muraleedharan A, D’hooge J, Bertrand A (2021) Fast linear least-squares method for ultrasound attenuation and backscatter estimation. Ultrasonics 116:106503CrossRef Birdi J, Muraleedharan A, D’hooge J, Bertrand A (2021) Fast linear least-squares method for ultrasound attenuation and backscatter estimation. Ultrasonics 116:106503CrossRef
14.
Zurück zum Zitat Liu J, Wang F, Zhao H, Han G (2017) Filtering algorithm and application of fuze echo signal based on LMS principle. J Proj Rock Missiles Guid 37(06):45–47 Liu J, Wang F, Zhao H, Han G (2017) Filtering algorithm and application of fuze echo signal based on LMS principle. J Proj Rock Missiles Guid 37(06):45–47
15.
Zurück zum Zitat Yang X-S (2009) Firefly algorithms for multimodal optimization. In: International symposium on Stochastic algorithm. pp169–178 Yang X-S (2009) Firefly algorithms for multimodal optimization. In: International symposium on Stochastic algorithm. pp169–178
16.
Zurück zum Zitat Yang X-S (2010) A new metaheuristic bat-inspired algorithm: nature inspired cooperative strategies for optimization. Springer, Berlin, pp 65–74MATH Yang X-S (2010) A new metaheuristic bat-inspired algorithm: nature inspired cooperative strategies for optimization. Springer, Berlin, pp 65–74MATH
19.
Zurück zum Zitat Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69(7):46–61CrossRef Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69(7):46–61CrossRef
20.
Zurück zum Zitat Heidari AA, Pahlavani P (2017) An efficient modified grey wolf optimizer with Lévy flight for optimization tasks. Appl Soft Comput J 60:115–134CrossRef Heidari AA, Pahlavani P (2017) An efficient modified grey wolf optimizer with Lévy flight for optimization tasks. Appl Soft Comput J 60:115–134CrossRef
22.
Zurück zum Zitat Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513CrossRef Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513CrossRef
23.
Zurück zum Zitat Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47CrossRef Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47CrossRef
24.
Zurück zum Zitat Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52–67CrossRef Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52–67CrossRef
25.
Zurück zum Zitat Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83(Sup 1):80–98CrossRef Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83(Sup 1):80–98CrossRef
26.
Zurück zum Zitat Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872CrossRef Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872CrossRef
28.
Zurück zum Zitat Chetty S, Adewumi AO (2014) Comparison study of swarm intelligence techniques for the annual crop planning problem. IEEE Trans Evol Comput 18(2):258–268CrossRef Chetty S, Adewumi AO (2014) Comparison study of swarm intelligence techniques for the annual crop planning problem. IEEE Trans Evol Comput 18(2):258–268CrossRef
29.
Zurück zum Zitat Fister I, Yang XS, Brest J et al (2015) Analysis of randomisation methods in swarm intelligence. Int J Bioinspir Comput 7(1):36–49 Fister I, Yang XS, Brest J et al (2015) Analysis of randomisation methods in swarm intelligence. Int J Bioinspir Comput 7(1):36–49
30.
Zurück zum Zitat Lalwani S, Kumar R, Deep K (2017) Multi-objective two level swarm intelligence approach for multiple RNA sequence structure alignment. Swarm Evol Comput 34:130–144CrossRef Lalwani S, Kumar R, Deep K (2017) Multi-objective two level swarm intelligence approach for multiple RNA sequence structure alignment. Swarm Evol Comput 34:130–144CrossRef
31.
Zurück zum Zitat Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci 181(23):5227–5239 CrossRef Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci 181(23):5227–5239 CrossRef
32.
Zurück zum Zitat Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95(5):51–67CrossRef Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95(5):51–67CrossRef
33.
Zurück zum Zitat Khaled M, Samir S, Abdelghani B (2018) Whale optimization algorithm based optimal reactive power dispatch: a case study of the Algerian power system. Electr Power Syst Res 163(10):696–750 Khaled M, Samir S, Abdelghani B (2018) Whale optimization algorithm based optimal reactive power dispatch: a case study of the Algerian power system. Electr Power Syst Res 163(10):696–750
34.
Zurück zum Zitat Yu Y, Wang H, Li N et al (2017) Automatic carrier landing system based on active disturbance rejection control with a novel parameters optimizer. Aerosp Sci Technol 69(10):149–160CrossRef Yu Y, Wang H, Li N et al (2017) Automatic carrier landing system based on active disturbance rejection control with a novel parameters optimizer. Aerosp Sci Technol 69(10):149–160CrossRef
35.
Zurück zum Zitat Huiling C, Yueting X, Mingjing W, Xuehua Z (2019) A balanced whale optimization algorithm for constrained engineering design problems. Appl Math Model 71:45–59MathSciNetCrossRefMATH Huiling C, Yueting X, Mingjing W, Xuehua Z (2019) A balanced whale optimization algorithm for constrained engineering design problems. Appl Math Model 71:45–59MathSciNetCrossRefMATH
36.
Zurück zum Zitat Mohammad T, Mohammad AM, Abushariah NI, Ibrahim A (2019) Improved whale optimization algorithm for feature selection in Arabic sentiment analysis. Appl Intell 49:1688–1707CrossRef Mohammad T, Mohammad AM, Abushariah NI, Ibrahim A (2019) Improved whale optimization algorithm for feature selection in Arabic sentiment analysis. Appl Intell 49:1688–1707CrossRef
37.
Zurück zum Zitat Ying LING, Yongquan ZHOU, Qifang LUO (2017) Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access 5:6168–6186CrossRef Ying LING, Yongquan ZHOU, Qifang LUO (2017) Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access 5:6168–6186CrossRef
42.
Zurück zum Zitat Kang SL, Zong WG (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3933CrossRefMATH Kang SL, Zong WG (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3933CrossRefMATH
43.
Zurück zum Zitat Reynolds AM, Frye MA (2007) Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search. PLoS One 2:e354CrossRef Reynolds AM, Frye MA (2007) Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search. PLoS One 2:e354CrossRef
44.
Zurück zum Zitat Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. International conference on computational intelligence for modelling, Vienna, Austria, pp 695–701. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. International conference on computational intelligence for modelling, Vienna, Austria, pp 695–701.
45.
46.
Zurück zum Zitat Yelghi A, Köse C (2018) A modified firefly algorithm for global minimum optimization. Appl Soft Comput 62:29–44CrossRef Yelghi A, Köse C (2018) A modified firefly algorithm for global minimum optimization. Appl Soft Comput 62:29–44CrossRef
47.
Zurück zum Zitat Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35CrossRef Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35CrossRef
Metadaten
Titel
A novel enhanced global exploration whale optimization algorithm based on Lévy flights and judgment mechanism for global continuous optimization problems
verfasst von
Jianxun Liu
Jinfei Shi
Fei Hao
Min Dai
Publikationsdatum
24.03.2022
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 4/2023
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-022-01638-1