Top

Engineering with Computers

Published in:

02-03-2020 | Original Article

HMOSHSSA: a hybrid meta-heuristic approach for solving constrained optimization problems

Authors: Satnam Kaur, Lalit K. Awasthi, A. L. Sangal

Published in: Engineering with Computers | Issue 4/2021

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

search-config

AI-assisted search

Off

Abstract

This paper proposes a novel hybrid multi-objective optimization algorithm named HMOSHSSA by synthesizing the strengths of Multi-objective Spotted Hyena Optimizer (MOSHO) and Salp Swarm Algorithm (SSA). HMOSHSSA utilizes the exploration capability of MOSHO to explore the search space effectively and leader and follower selection mechanism of SSA to achieve global best solution with faster convergence. The proposed algorithm is evaluated on 24 benchmark test functions, and its performance is compared with seven well-known multi-objective optimization algorithms. The experimental results demonstrate that HMOSHSSA acquires very competitive results and outperforms other algorithms in terms of convergence speed, search-ability and accuracy. Additionally, HMOSHSSA is also applied on seven well-known engineering problems to further verify its efficacy. The results reveal the effectiveness of proposed algorithm toward solving real-life multi-objective optimization problems.

previous article Improved singular boundary method and dual reciprocity method for fractional derivative Rayleigh–Stokes problem

next article Symbiotic polyhedron operation tree (SPOT) for elastic modulus formulation of recycled aggregate concrete

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

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

Available only for authorised users

Bandaru S, Deb K (2016) Metaheuristic techniques. In: Sengupta RN, Gupta A, Dutta J (eds) Decision sciences: theory and practice. Taylor & Francis Group, CRC Press, pp 693–750

Dhiman G, Kumar V (2018) Multi-objective spotted hyena optimizer: a multi-objective optimization algorithm for engineering problems. Knowl-Based Syst 150:175–197

Dhiman G, Guo S, Kaur S (2018) ED-SHO: a framework for solving nonlinear economic load power dispatch problem using spotted hyena optimizer. Mod Phys Lett A 33(40):1850239

Kaur A, Kaur S, Dhiman G (2018) A quantum method for dynamic nonlinear programming technique using Schrödinger equation and monte carlo approach. Mod Phys Lett B 32(30):1850374

Chiandussi G, Codegone M, Ferrero S, Varesio FE (2012) Comparison of multi-objective optimization methodologies for engineering applications. Comput Math Appl 63(5):912–942MathSciNetMATH

Branke J, Deb K, Dierolf H, Osswald M (2004) Finding knees in multi-objective optimization. In: International conference on parallel problem solving from nature. Springer, pp 722–731

Coello CAC, Lamont GB, Van Veldhuizen DA et al (2007) Evolutionary algorithms for solving multi-objective problems, vol 5. Springer, BerlinMATH

Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscip Optim 26(6):369–395MathSciNetMATH

Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91(9):992–1007

10.

Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on evolutionary computation, CEC’02, pp 1051–1056

11.

Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

12.

Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

13.

Corne,DW, Jerram NR, Knowles JD, Oates MJ (2001) PESA-II: region-based selection in evolutionary multiobjective optimization. In: Proceedings of the 3rd annual conference on genetic and evolutionary computation. Morgan Kaufmann Publishers Inc., pp 283–290

14.

Zitzler E, Laumanns M, Thiele L (2001) SPEA 2: improving the strength Pareto evolutionary algorithm. TIK-report 103

15.

Angus D, Woodward C (2009) Multiple objective ant colony optimisation. Swarm Intell 3(1):69–85

16.

Mansoor U, Kessentini M, Maxim BR, Deb K (2017) Multi-objective code-smells detection using good and bad design examples. Softw Qual J 25(2):529–552

17.

Chen M, Hammami O (2015) A system engineering conception of multi-objective optimization for multi-physics system. In: Multiphysics modelling and simulation for systems design and monitoring. Springer, pp 299–306

18.

Luh G-C, Chueh C-H (2004) Multi-objective optimal design of truss structure with immune algorithm. Comput Struct 82(11–12):829–844MathSciNet

19.

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 114:163–191

20.

Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70

21.

Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

22.

Zhang Q, Zhou A, Zhao S, Suganthan PN, Liu W, Tiwari S (2009) 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, 264

23.

Deb K, Thiele L, Laumanns M, Zitzler E (2005) Scalable test problems for evolutionary multiobjective optimization. Springer, London, pp 105–145MATH

24.

Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195

25.

Deb K (2014) Multi-objective optimization. In: Search methodologies. Springer, pp 403–449

26.

Mirjalili SZ, Mirjalili S, Saremi S, Faris H, Aljarah I (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48(4):805–820

27.

Edgeworth FY (1881) Mathematical physics: P. Keagan, London

28.

Pareto V (1964) Cours d’economie politique: Librairie Droz

29.

Coello CAC (2009) Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored. Front Comput Sci China 3(1):18–30

30.

Zhou A, Bo-Yang Q, Li H, Zhao S-Z, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evolut Comput 1(1):32–49

31.

Mirjalili S, Saremi S, Mirjalili SM, Coelho LS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119

32.

Zitzler E, Thiele L (1998) An evolutionary algorithm for multiobjective optimization: the strength pareto approach. TIK-report 43

33.

Corne DW, Knowles JD, Oates MJ (2000) The Pareto envelope-based selection algorithm for multiobjective optimization. In: International conference on parallel problem solving from nature. Springer, pp 839–848

34.

Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248

35.

Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science. MHS’95. IEEE, pp 39–43

36.

Hancer E, Xue B, Zhang M, Karaboga D, Akay B (2015) A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp 2420–2427

37.

Shang R, Liu H, Jiao L (2017) Multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks. Physica A 486(Supplement C):1–24

38.

Pradhan PM, Panda G (2012) Solving multiobjective problems using cat swarm optimization. Expert Syst Appl 39(3):2956–2964

39.

Zhong W, Liu J, Xue M, Jiao L (2004) A multiagent genetic algorithm for global numerical optimization. IEEE Trans Syst Man Cybern Part B (Cybern) 34(2):1128–1141

40.

Yang X-S, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237MathSciNet

41.

Yang S, Wang M, Jiao L (2004) A quantum particle swarm optimization. In: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), vol 1, pp 320–324

42.

Mejia JAH, Schütze O, Cuate O, Lara A, Deb K (2017) RDS-NSGA-II: a memetic algorithm for reference point based multi-objective optimization. Eng Optim 49(5):828–845MathSciNet

43.

Jiao L, Member S, Wang L (2000) A novel genetic algorithm based on immunity. IEEE Trans Syst Man Cybern Part A Syst Hum 30:552–561

44.

Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279

45.

Knowles JD, Corne DW (2000) Approximating the nondominated front using the pareto archived evolution strategy. Evol Comput 8(2):149–172

46.

Zhang Z, Ding S, Jia W (2019) A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Eng Appl Artif Intell 85:254–268

47.

Sree Ranjini KS, Murugan S (2017) Memory based hybrid dragonfly algorithm for numerical optimization problems. Expert Syst Appl 83:63–78

48.

Chegini SN, Bagheri A, Najafi F (2018) PSOSCALF: a new hybrid pso based on sine cosine algorithm and Levy flight for solving optimization problems. Appl Soft Comput 73:697–726

49.

Katunin An, Przystalka P (2014) Meta-optimization method for wavelet-based damage identification in composite structures. In: 2014 federated conference on computer science and information systems. IEEE, pp 429–438

50.

Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput 3(4):257–271

51.

Coello CAC, Dhaenens C, Jourdan L (2010) Multi-objective combinatorial optimization: problematic and context. Springer, Berlin, pp 1–21MATH

52.

Schutze O, Esquivel X, Lara A, Coello CAC (2012) Using the averaged Hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans Evol Comput 16(4):504–522

53.

Rudolph G, Schütze O, Grimme C, Domínguez-Medina C, Trautmann H (2016) Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results. Comput Optim Appl 64(2):589–618MathSciNetMATH

54.

Li M, Zheng J (2009) Spread assessment for evolutionary multi-objective optimization. Springer, Berlin, pp 216–230

55.

Zitzler E, Thiele L, Laumanns M, Fonseca CM, da Fonseca VG (2003) Performance assessment of multiobjective optimizers: an analysis and review. Trans Evolut Comput 7(2):117–132

56.

Knowles JD, Thiele L, Zitzler E (2006) A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK-Report 214

57.

Kaur S, Singh P (2019) How does object-oriented code refactoring influence software quality? Research landscape and challenges. J Syst Softw 157:110394

58.

Mkaouer MW, Kessentini M, Cinnéide MÓ, Hayashi S, Deb K (2017) A robust multi-objective approach to balance severity and importance of refactoring opportunities. Empir Softw Eng 22(2):894–927

59.

Fokaefs M, Tsantalis N, Chatzigeorgiou A (2007) Jdeodorant: Identification and removal of feature envy bad smells. In: 2007 IEEE international conference on software maintenance. IEEE, pp 519–520

60.

Ouni A, Kessentini M, Sahraoui H, Hamdi MS (2011) Design defects detection and correction by example. In: 2011 IEEE international conference on program comprehension (ICPC). IEEE, pp 81–90

61.

Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127

62.

Kannan BK, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des 116(2):405–411

63.

Siddall JN (1972) Analytical decision-making in engineering design. Prentice Hall, Upper Saddle River

Title: HMOSHSSA: a hybrid meta-heuristic approach for solving constrained optimization problems
Authors: Satnam Kaur
Lalit K. Awasthi
A. L. Sangal
Publication date: 02-03-2020
Publisher: Springer London
Published in: Engineering with Computers / Issue 4/2021
Print ISSN: 0177-0667
Electronic ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-020-00989-x

Springer Professional

Abstract

Please log in to get access to your license.

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

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2021

Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems

Dynamic stability of viscoelastic nanotubes conveying pulsating magnetic nanoflow under magnetic field

New formulation of the orthonormal Bernoulli polynomials for solving the variable-order time fractional coupled Boussinesq–Burger’s equations

Vibration analysis of porous metal foam plates rested on viscoelastic substrate

Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill

A high-performance four-node flat shell element with drilling degrees of freedom