Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Engineering with Computers
Erschienen in: Engineering with Computers 5/2022

22.08.2021 | Original Article

LSFQPSO: quantum particle swarm optimization with optimal guided Lévy flight and straight flight for solving optimization problems

verfasst von: Xiaoyan Liu, Gai-Ge Wang, Ling Wang

Erschienen in: Engineering with Computers | Sonderheft 5/2022

Einloggen

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

search-config
loading …

Abstract

As a metaheuristic algorithm, particle swarm optimization (PSO) has two main disadvantages. Firstly, it needs to set many parameters, which is not conducive to finding the optimal parameters of the model to be optimized. Secondly, it is easy to fall into the trap of local optimal. Motivated by concepts in quantum mechanics and PSO, quantum-behaved particle swarm optimization (QPSO) was proposed having better global search ability. However, QPSO is deficient in solving high-dimensional problems and performs poorly in adaptability. In this paper, in order to better solve the high-dimensional problems and more applicable to real-world optimization problems, two strategies of Lévy flight (LF) and straight flight (SF) are introduced. An improved quantum particle swarm optimization with Lévy flight and straight flight (LSFQPSO) is proposed. The proposed LSFQPSO algorithm is tested on 22 classic benchmark functions and three engineering optimization problems. The obtained results are compared with seven metaheuristic algorithms and evaluated according to Friedman rank test. The experiments show that LSFQPSO algorithm provides better results with superior performance in most tests compared with seven well-known algorithms, especially in solving high-dimensional problems. What’s more, the proposed LSFQPSO algorithm also shows good performance in solving real-world engineering design optimization problems.
Vorheriger Artikel Vibration of a three-phase bidirectional functionally graded sandwich beam carrying a moving mass using an enriched beam element

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Literatur
1.
Wang GG, Tan Y (2019) Improving metaheuristic algorithms with information feedback models. IEEE Trans Cybern 49(2):542–555CrossRef
2.
Li J, Li YX, Tian SS (2020) An improved cuckoo search algorithm with self-adaptive knowledge learning. Neural Comput Appl 32(16):11967–11997CrossRef
3.
Wang GG, Cai X, Cui Z, Min G, Chen J (2020) High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. IEEE Trans Emerg Top Comput 8(1):20–30
4.
Wang F, Li Y, Zhou A (2019) An estimation of distribution algorithm for mixed-variable newsvendor problems. IEEE Trans Evol Comput 24(3):479–493
5.
Gao D, Wang GG, Pedrycz W (2020) Solving fuzzy job-shop scheduling problem using DE algorithm improved by a selection mechanism. IEEE Trans Fuzzy Syst 28(12):3265–3275CrossRef
6.
Chen S, Chen R, Wang GG, Gao J, Sangaiah AK (2018) An adaptive large neighborhood search heuristic for dynamic vehicle routing problems. Comput Electr Eng 67:596–607CrossRef
7.
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133CrossRef
8.
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191CrossRef
9.
Li W, Wang GG, Alavi AH (2020) Learning-based elephant herding optimization algorithm for solving numerical optimization problems. Knowl Based Syst 195:105675CrossRef
10.
Li W, Wang G-G (2021) Elephant herding optimization using dynamic topology and biogeography-based optimization based on learning for numerical optimization. Eng Comput 20(21):1–29
11.
Wang F, Zhang H, Zhou A (2021) A particle swarm optimization algorithm for mixed-variable optimization problems. Swarm Evol Comput 60:100808CrossRef
12.
Mirjalili S, Jangir P, Mirjalili SZ, Saremi S, Trivedi IN (2017) Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowl Based Syst 134:50–71CrossRef
13.
Mirjalili S, Lewis A (2015) Novel performance metrics for robust multi-objective optimization algorithms. Swarm Evol Comput 21:1–23CrossRef
14.
Rong M, Gong D, Zhang Y, Jin Y, Pedrycz W (2019) Multidirectional prediction approach for dynamic multiobjective optimization problems. IEEE Trans Cybern 49(9):3362–3374CrossRef
15.
Sun J, Miao Z, Gong D, Zeng XJ, Li J, Wang GG (2020) Interval multiobjective optimization with memetic algorithms. IEEE Trans Cybern 50(8):3444–3457CrossRef
16.
Gu ZM, Wang GG (2020) Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization. Futur Gener Comput Syst 107:49–69CrossRef
17.
Zhang Y, Wang GG, Li K, Yeh WC, Jian M, Dong J (2020) Enhancing MOEA/D with information feedback models for large-scale many-objective optimization. Inf Sci 522:1–16MathSciNetMATHCrossRef
18.
Wang F, Li Y, Liao F, Yan H (2020) An ensemble learning based prediction strategy for dynamic multi-objective optimization. Appl Soft Comput 96:106592CrossRef
19.
Cao Y, Zhang H, Li W, Zhou M, Zhang Y, Chaovalitwongse WA (2019) Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions. IEEE Trans Evol Comput 23(4):718–731CrossRef
20.
Beni G, Wang J (1989) Swarm intelligence in cellular robotic systems. In: NATO advanced workshop robots biological system, Springer, pp 703–712
21.
Kennedy J, Eberhart R (1995) Particle swarm optimization (PSO). In: IEEE international conference on neural networks, IEEE, pp 1942–1948
22.
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B (Cybern) 26(1):29–41CrossRef
23.
Wang GG, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Appl 31(7):1995–2014CrossRef
24.
Li J, Lei H, Alavi AH, Wang GG (2020) Elephant herding optimization: variants, hybrids, and applications. Mathematics 8(9):1415CrossRef
25.
26.
Liu B, Wang L, Jin YH (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cybern Part B (Cybern) 37(1):18–27CrossRef
27.
Sun J, Fang W, Wu X, Palade V, Xu W (2012) Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection. Evol Comput 20(3):349–393CrossRef
28.
Xi M, Sun J, Xu W (2008) An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position. Appl Math Comput 205(2):751–759MATH
29.
Sun J, Fang W, Palade V, Wu X, Xu W (2011) Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point. Appl Math Comput 218(7):3763–3775MATH
30.
Yang S, Wang M (2004) A quantum particle swarm optimization. In: 2004 IEEE congress on evolutionary computation (CEC 2004), IEEE, pp 320–324
31.
Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behavior. In: 2004 IEEE congress on evolutionary computation (CEC 2004), IEEE, pp 325–331
32.
Wang GG, Chang B, Zhang Z (2015) A multi-swarm bat algorithm for global optimization. In: 2015 IEEE congress on evolutionary computation (CEC 2015), IEEE, pp 480–485
33.
Wang GG, Gandomi AH, Alavi AH (2014) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 38(9–10):2454–2462MathSciNetMATHCrossRef
34.
Li ZY, Yi JH, Wang GG (2015) A new swarm intelligence approach for clustering based on krill herd with elitism strategy. Algorithms 8(4):951–964MathSciNetMATHCrossRef
35.
36.
Wang GG, Deb S, Gandomi AH, Zhang Z, Alavi AH (2015) Chaotic cuckoo search. Soft Comput 20(9):3349–3362CrossRef
37.
Rameshkumar K, Suresh RK, Mohanasundaram KM (2005) Discrete particle swarm optimization (DPSO) algorithm for permutation flowshop scheduling to minimize makespan. In: International conference on natural computation, Springer, Berlin, pp 572–581
38.
Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. In: International conference on evolutionary programming, Springer, Berlin, pp 591–600
39.
Angeline PJ (1998) Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. In: International conference on evolutionary programming, Springer, Berlin, pp 601–610
40.
Jong-Bae P, Yun-Won J, Joong-Rin S, Lee KY (2010) An improved particle swarm optimization for nonconvex economic dispatch problems. IEEE Trans Power Syst 25(1):156–166CrossRef
41.
Jang-Ho S, Chang-Hwan I, Sang-Yeop K, Cheol-Gyun L, Hyun-Kyo J (2008) An improved particle swarm optimization algorithm mimicking territorial dispute between groups for multimodal function optimization problems. IEEE Trans Magn 44(6):1046–1049CrossRef
42.
Pan M, Thangaraj R, Grosan G (2008) Improved particle swarm optimization with low-discrepancy. In: 2008 IEEE congress on evolutionary computation (CEC 2008), IEEE, pp 3011–3018
43.
Agrawal RK, Kaur B, Agarwal P (2021) Quantum inspired particle swarm optimization with guided exploration for function optimization. Appl Soft Comput 102:107122CrossRef
44.
dos Coelho LS, Mariani VC (2008) Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects. Energy Convers Manag 49(11):3080–3085CrossRef
45.
dos Coelho LS (2008) A quantum particle swarm optimizer with chaotic mutation operator. Chaos Solitons Fractals 37(5):1409–1418CrossRef
46.
Sabat SL, dos Coelho LS, Abraham A (2009) MESFET DC model parameter extraction using quantum particle swarm optimization. Microelectron Reliab 49(6):660–666CrossRef
47.
Sun J, Wu X, Palade V, Fang W, Lai C-H, Xu W (2012) Convergence analysis and improvements of quantum-behaved particle swarm optimization. Inf Sci 193:81–103MathSciNetCrossRef
48.
Mariani VC, Duck ARK, Guerra FA, dos Coelho LS, Rao RV (2012) A chaotic quantum-behaved particle swarm approach applied to optimization of heat exchangers. Appl Therm Eng 42:119–128CrossRef
49.
Li L, Jiao L, Zhao J, Shang R, Gong M (2017) Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering. Pattern Recogn 63:1–14CrossRef
50.
Vaze R, Deshmukh N, Kumar R, Saxena A (2021) Development and application of quantum entanglement inspired particle swarm optimization. Knowl Based Syst 219:106859CrossRef
51.
Kumar N, Shaikh AA, Mahato SK, Bhunia AK (2021) Applications of new hybrid algorithm based on advanced Cuckoo search and adaptive Gaussian quantum behaved particle swarm optimization in solving ordinary differential equations. Expert Syst Appl 172:114646CrossRef
52.
Lu X-L, He G (2021) QPSO algorithm based on Lévy flight and its application in fuzzy portfolio. Appl Soft Comput 99:106894CrossRef
53.
Song W, Cattani C, Chi C-H (2020) Multifractional brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: an integrated approach. Energy 194:116847CrossRef
54.
Gölcük İ, Ozsoydan FB (2021) Quantum particles-enhanced multiple harris hawks swarms for dynamic optimization problems. Expert Syst Appl 167:114202CrossRef
55.
Senthilnath J, Das V, Omkar SN, Mani V (2013) Clustering using levy flight cuckoo search. In: Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012), Springer, pp 65–75
56.
Yang X (2010) Firefly algorithm, Lévy flights and global optimization. Res Dev Intell Syst 26:209–218
57.
Reynolds AM, Reynolds DR, Smith AD, Svensson GP, Lofstedt C (2007) Appetitive flight patterns of male agrotis segetum moths over landscape scales. J Theor Biol 245(1):141–149MathSciNetMATHCrossRef
58.
Gomes AS, Raposo EP, Moura AL, Fewo SI, Pincheira PI, Jerez V, Maia LJ, de Araujo CB (2016) Observation of Levy distribution and replica symmetry breaking in random lasers from a single set of measurements. Sci Rep 6:27987CrossRef
59.
Charin C, Ishak D, Zainuri MAAM, Ismail B (2021) Modified levy flight optimization for a maximum power point tracking algorithm under partial shading. Appl Sci 11(3):992CrossRef
60.
Haklı H, Uğuz H (2014) A novel particle swarm optimization algorithm with levy flight. Appl Soft Comput 23:333–345CrossRef
61.
Li X, Yin M (2015) Modified cuckoo search algorithm with self adaptive parameter method. Inf Sci 298:80–97CrossRef
62.
Henderson D, Jacobson SH, Johnson AW (2003) The theory and practice of simulated annealing. Handbook of Metaheuristics. Springer, pp 287–319CrossRef
63.
Wang GG, Gandomi AH, Alavi AH, Deb S (2015) A hybrid method based on krill herd and quantum-behaved particle swarm optimization. Neural Comput Appl 27(4):989–1006CrossRef
64.
Tian N, Lai CH (2013) Parallel quantum-behaved particle swarm optimization. Int J Mach Learn Cybern 5(2):309–318MathSciNetCrossRef
65.
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73CrossRef
66.
Derrac J, García S, Hui S, Suganthan PN, Herrera F (2014) Analyzing convergence performance of evolutionary algorithms: a statistical approach. Inf Sci 289:41–58CrossRef
67.
Carrasco J, García S, Rueda MM, Das S, Herrera F (2020) Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: practical guidelines and a critical review. Swarm Evol Comput 54:100665CrossRef
68.
Kumar A, Misra RK, Singh D (2017) Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase. In: 2017 IEEE congress on evolutionary computation (CEC 2017), pp 1835–1842
69.
Brest J, Maučec MS, Bošković B (2017) Single objective real-parameter optimization: algorithm jSO. In: 2017 IEEE congress on evolutionary computation (CEC 2017), IEEE, pp 1311–1318
70.
Awad NH, Ali MZ, Suganthan PN (2017) Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems. In: 2017 IEEE congress on evolutionary computation (CEC 2017), IEEE, pp 372–379
71.
Kannan BK, Kramer SN (1994) An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. Trans ASME J Mech Des 116:405–411CrossRef
72.
Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35CrossRef
73.
Mezura-Montes E, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37(4):443–473MathSciNetMATHCrossRef
74.
Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27(1):155–182MATHCrossRef
75.
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249CrossRef
76.
Kaveh A, Talatahari S (2009) Engineering optimization with hybrid particle swarm and ant colony optimization. Asian J Civ Eng 10:611–628
77.
Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014CrossRef
78.
Deb K, Goyal M (1996) A combined genetic adaptive search (GeneAS) for engineering design. Comput Sci Inform 26(4):30–45
79.
Coello CAC, Montes EM (2002) Constraint- handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16:193–203CrossRef
80.
He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99CrossRef
81.
Raj KH, Sharma RS (2005) An evolutionary computational technique for constrained optimisation in engineering design. J Inst Eng India Part Mech Eng Div 86:121–128
82.
Mezura-Montes E, Carlos A, Coello C, Reyes JV, Dávila LM (2007) Multiple trial vectors in differential evolution for engineering design. Eng Optim 39(5):567–589MathSciNetCrossRef
83.
Carlos A, Coello C (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127CrossRef
84.
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2–4):311–338MATHCrossRef
85.
Mirjalili S, Lewis A (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput Appl 25(7–8):1569–1584CrossRef
86.
Yu C, Cai Z, Ye X, Wang M, Zhao X, Liang G, Chen H, Li C (2020) Quantum-like mutation-induced dragonfly-inspired optimization approach. Math Comput Simul 178:259–289MathSciNetMATHCrossRef
87.
Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294CrossRef
88.
Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36–38):3902–3933MATHCrossRef
89.
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRef
90.
Li X, Yin M (2013) Multiobjective binary biogeography based optimization for feature selection using gene expression data. IEEE Trans Nanobiosci 12(4):343–353CrossRef
Metadaten
Titel
LSFQPSO: quantum particle swarm optimization with optimal guided Lévy flight and straight flight for solving optimization problems
verfasst von
Xiaoyan Liu
Gai-Ge Wang
Ling Wang
Publikationsdatum
22.08.2021
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe Sonderheft 5/2022
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-021-01497-2

Weitere Artikel der Sonderheft 5/2022

Engineering with Computers 5/2022 Zur Ausgabe

Original Article

An enhanced Cauchy mutation grasshopper optimization with trigonometric substitution: engineering design and feature selection

Original Article

A novel enhanced exploration firefly algorithm for global continuous optimization problems

Original Article

Novel approach to evaluate rock mass fragmentation in block caving using unascertained measurement model and information entropy with flexible credible identification criterion

Original Article

A comprehensive mathematical simulation of the composite size-dependent rotary 3D microsystem via two-dimensional generalized differential quadrature method

Original Article

Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings

Original Article

Global sensitivity analysis of failure probability of structures with uncertainties of random variable and their distribution parameters

Neuer Inhalt

    Bildnachweise